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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis Snowflake Quickstart introduces you to the basics of using Snowflake together with Dataiku Cloud as part of a Data Science project. We’ll be highlighting some of the well-integrated functionalities between the two technologies. It is designed specifically for use with the \u003Ca href=\"https://trial.snowflake.com\"\u003ESnowflake free 30-day trial\u003C/a\u003E, and the Dataiku Cloud free trial version via Snowflake’s Partner Connect.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe Use Case:\u003C/strong\u003E Recent advances in generative AI have made it easy to apply for jobs. But be careful! Scammers have also been known to create fake job applications in the hopes of stealing personal information. Let’s see if you — with Dataiku & Snowflake's help — can spot a real job posting from a fake one!\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAbout the data:\u003C/strong\u003E <br> The data for this quickstart comes from a Kaggle dataset of ~18000 job descriptions, out of which about 800 are fake. These are fairly simple datasets, once you have completed the lab you could consider enriching the project with additional data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUse of the Snowflake free 30-day trial environment\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, and database concepts and objects\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n","\u003Cp\u003EThe exercises in this lab will walk you through the steps to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate databases, tables, views, and warehouses in Snowflake\u003C/li\u003E\u003Cli\u003EUse Snowflake’s “Partner Connect” to seamlessly create a Dataiku DSS Cloud trial\u003C/li\u003E\u003Cli\u003ECreate a Data Science project in Dataiku and perform analysis on data via Dataiku within Snowflake\u003C/li\u003E\u003Cli\u003EUse both visual and code tools\u003C/li\u003E\u003Cli\u003ECreate, run, and evaluate simple Machine Learning models in Dataiku\u003C/li\u003E\u003Cli\u003EHow at each step of the data science process you can utilise Dataiku and Snowflake in tandem to accelerate your team\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat We’re Going To Build\u003C/h3\u003E\n","\u003Cp\u003EWe will build a project that uses input datasets from Snowflake. We’ll build a data science pipeline by applying data transformations, building a machine learning model, and deploying it to Dataiku's Flow. We will then see how you can score the model against fresh data from Snowflake and automate.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Your Lab Environment\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003EIf you haven’t already, register for a \u003Ca href=\"https://trial.snowflake.com/\"\u003ESnowflake free 30-day trial\u003C/a\u003E The rest of the sections in this lab assume you are using a new Snowflake account created by registering for a trial.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Please ensure that you use the \u003Cstrong\u003Esame email address\u003C/strong\u003E for both your Snowflake and Dataiku sign up\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERegion\u003C/strong\u003E  - Although not a requirement we'd suggest you select the region that is physically closest to you for this lab\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECloud Provider\u003C/strong\u003E  -  Although not a requirement we'd suggest you select \u003Ccode\u003EAWS\u003C/code\u003E for this lab\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake edition\u003C/strong\u003E  -  We suggest you select select the \u003Ccode\u003EEnterprise edition\u003C/code\u003E so you can leverage some advanced capabilities that are not available in the Standard Edition.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAfter activation, you will create a \u003Ccode\u003Eusername\u003C/code\u003Eand \u003Ccode\u003Epassword\u003C/code\u003E. Write down these credentials. \u003Cstrong\u003EBookmark this URL for easy, future access\u003C/strong\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAbout the screen captures, sample code, and environment:\u003C/strong\u003E <br> Screen captures in this lab depict examples and results that may slightly vary from what you may see when you complete the exercises.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Snowflake User Interface\u003C/h2\u003E\n","\u003Ch3\u003ELogging Into the Snowflake User Interface (UI)\u003C/h3\u003E\n","\u003Cp\u003EOpen a browser window and enter the URL of your Snowflake 30-day trial environment. You should see the login screen below. Enter your unique credentials to log in.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i1_login.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EClose any Welcome Boxes and Tutorials\u003C/h3\u003E\n","\u003Cp\u003EYou may see “welcome” and “helper” boxes in the UI when you log in for the first time. Close them by clicking on \u003Ccode\u003ESkip for now\u003C/code\u003E in the bottom right corner in the screenshot below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i2_welcome.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ENavigating the Snowflke UI\u003C/h3\u003E\n","\u003Cp\u003EFirst let’s get you acquainted with Snowflake! This section covers the basic components of the user interface to help you orient yourself. We will move left to right in the top of the UI.\u003C/p\u003E\n","\u003Cp\u003EThe main menu on the left allows you to switch between the different areas of Snowflake:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i3_menu.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EDatabases\u003C/strong\u003E tab shows information about the databases you have created or have privileges to access. You can create, clone, drop, or transfer ownership of databases as well as load data (limited) in the UI. Notice several databases already exist in your environment. However, we will not be using these in this lab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i4_databases.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab provides an interface for submitting code queries, performing DDL and DML operations and viewing results as your queries/operations complete.\u003C/p\u003E\n","\u003Cp\u003EIn the left pane is the database objects browser which enables users to explore all databases, schemas, tables, and views accessible by the role selected for a worksheet. The bottom pane will show results of queries and operations.\u003C/p\u003E\n","\u003Cp\u003EIf this is the first time you’ve used Snowsight, you might be prompted to enable it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/dataiku41.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i5_worksheets.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you can see, there have already some worksheets been prepared for you to work with the demo data in the databases that we saw before. However, we are not going to use these existing worksheets now.\u003C/p\u003E\n","\u003Cp\u003EInstead, we are going to create a new one. For that, please click on the blue \u003Ccode\u003E+\u003C/code\u003E Button in the top right corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i6_new_worksheet.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect \u003Ccode\u003ESQL Worksheet\u003C/code\u003E from the menu and a new worksheet will be created and shown.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i7_new_worksheet.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERename the newly created worksheet to \u003Cstrong\u003EJob Postings\u003C/strong\u003E by clicking on the worksheet name and typing \u003Ccode\u003EJob Postings\u003C/code\u003E and pressing ‘Enter’\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i7_worksheet_rename.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWorksheets vs the UI\u003C/strong\u003E <br> Much of the configurations in this lab will be executed via this pre-written SQL in the Worksheet in order to save time. These configurations could also be done via the UI in a less technical manner but would take more time.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EHistory\u003C/strong\u003E tab allows you to view the details of all queries executed in the last 14 days in the Snowflake account (click on a Query ID to drill into the query for more detail).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i8_query_history.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you click on the bottom left of the UI where your username appears, you will see that you can change your password, roles, or preferences. Snowflake has several system defined roles. You are currently in the default role of SYSADMIN. We will change this in the next part of the lab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i9_profile_accountadmin.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESYSADMIN\u003C/strong\u003E <br> For most of this lab you will remain in the SYSADMIN (aka System Administrator) role which has privileges to create warehouses and databases and other objects in an account. In a real-world environment, you would use different roles for the tasks in this lab, and assign the roles to your users. More on access control in Snowflake is in towards the end of this lab and also in our \u003Ca href=\"https://docs.snowflake.com/user-guide/security-access-control-overview\"\u003Edocumentation\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Dataiku Trial Account Via Snowflake Partner Connect\u003C/h2\u003E\n","\u003Ch3\u003ECreate Dataiku trial via Partner Connect\u003C/h3\u003E\n","\u003Cp\u003EAt the top right of the page, confirm that your current role is \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, by clicking on your profile on the top right.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EData Products\u003C/code\u003E on the left-hand menu\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EPartner Connect\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESearch for Dataiku\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003EDataiku\u003C/code\u003E tile\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_1.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EDepending on which screen you are on you may not see the full menu as above but hovering over\nthe Data Products (Cloud) icon will show the options\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis will automatically create the connection parameters required for Dataiku to connect to Snowflake. Snowflake will create a dedicated database, warehouse, system user, system password and system role, with the intention of those being used by the Dataiku account.\u003C/p\u003E\n","\u003Cp\u003EFor this lab we’d like to use the \u003Cstrong\u003EPC_DATAIKU_USER\u003C/strong\u003E to connect from Dataiku to Snowflake, and use the \u003Cstrong\u003EPC_DATAIKU_WH\u003C/strong\u003E when performing activities within Dataiku that are pushed down into Snowflake.\u003C/p\u003E\n","\u003Cp\u003EThis is to show that a Data Science team working on Dataiku and by extension on Snowflake can work completely independently from the Data Engineering team that works on loading data into Snowflake using different roles and warehouses.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_9_partner_connect_connect.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENote that the user password (which is autogenerated by Snowflake and never displayed), along with all of the other Snowflake connection parameters, are passed to the Dataiku server so that they will automatically be used for the Dataiku connection for this lab. \u003Cstrong\u003EDO NOT CHANGE THESE\u003C/strong\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ccode\u003EConnect\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EYou will get a pop-ip which tells you your partner account has been created. Click on \u003Ccode\u003EActivate\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInformational Note:\u003C/strong\u003E <br> If you are using a different Snowflake account than the one created\nat the start, you may get a screen asking for your email details. Click on ‘Go to Preferences’ and\npopulate with your email details\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis will launch a new page that will redirect you to a launch page from Dataiku.\u003C/p\u003E\n","\u003Cp\u003EHere, you will have two options:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELogin with an existing Dataiku username\u003C/li\u003E\u003Cli\u003ESign up for a new Dataiku account\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe assume that you’re new to Dataiku, so ensure the “Sign Up” box is selected, and sign up with either GitHub, Google or your email address and your new password. Click sign up.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen using your email address, ensure your password fits the following criteria:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAt least 8 characters in length\u003C/li\u003E\u003Cli\u003EShould contain:\nLower case letters (a-z)\nUpper case letters (A-Z)\nNumbers (i.e. 0-9)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should have received an email from Dataiku to the email you have signed up with. Activate your Dataiku account via the email sent.\u003C/p\u003E\n","\u003Ch3\u003EReview Dataiku Setup\u003C/h3\u003E\n","\u003Cp\u003EUpon clicking on the activation link, please briefly review the Terms of Service of Dataiku Cloud. In order to do so, please scroll down to the bottom of the page. Click on \u003Ccode\u003EI AGREE\u003C/code\u003E and then click on \u003Ccode\u003ENEXT\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_3.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EComplete your sign up some information about yourself and then click on \u003Ccode\u003EStart\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EYou will be redirected to the Dataiku Cloud Launchpad site. Click \u003Ccode\u003EGOT IT!\u003C/code\u003E to continue.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_4.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is the Cloud administration console where you can perform tasks such as inviting other users to collaborate, add plugin extensions, install industry solutions to accelerate projects as well as access community and academy resources to help your learning journey.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E It may take several minutes for your instance to Dataiku to start up the first time,\nduring this time you will not be able to add the extension as described below.\nYou can always come back to this task later if time doesn't allow now\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIt's beyond the scope of this course to cover these but for this lab we would like to enable a few of the AI Assistants so lets do that now.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EExtensions\u003C/code\u003E on the left menu\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003E+ ADD AN EXTENSION\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFind \u003Ccode\u003EAI Services\u003C/code\u003E and click on it\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIn the AI Services Extension screen perform the following tasks:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAgree to the terms and services\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EEnable AI Prepare\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EEnable AI Explain\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003EADD\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EGo Back To Space\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_5a.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou’ve now successfully set up your Dataiku trial account via Snowflake’s Partner Connect. We are now ready to continue with the lab. For this, move back to your Snowflake browser.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPreparing And Exploring The Data In Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EAnalysing the data using Snowsight\u003C/h3\u003E\n","\u003Cp\u003ENow that we’ve done some preparation work, let’s do some primarily data analysis on our data. For this we will use Snowsight, the SQL Worksheets replacement, which is designed to support data analyst activities.\u003C/p\u003E\n","\u003Cp\u003ESnowflake recently released the next generation of it’s analytics UI — \u003Cstrong\u003ESnowsight\u003C/strong\u003E. On top of a redesigned interface, there are many improvements for analysts, data engineers, and business users. With Snowsight, it is easier and faster to write queries and get results and collaboration with others through sharing makes it easier to explore and visualize data across your organization. Snowsight includes many features and enhancements, including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFast query writing:\u003C/strong\u003E Includes smart autocomplete for query syntax keywords or listing values that match table/column names, data filters and quick access to Snowflake documentation for specific functions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive query results:\u003C/strong\u003E View summary statistics about the data that has been returned by their query, using histograms of the distribution to identify outliers and anomalies.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAttractive data visualizations:\u003C/strong\u003E Quickly analyze data without requiring an external analytics/visualization tool, with automatic chart generation and drag-and-drop interface for creating dashboards.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESharing and collaboration:\u003C/strong\u003E Share queries, worksheets, visualizations and dashboards securely among teams.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema browser:\u003C/strong\u003E Search instantly across databases and schemas accessible by the current session role for tables, views, and columns whose names contain a specified string. Pin tables for quick reference to see column names and data types.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor more information on using Snowsight, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html\"\u003Edocumentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ELet’s run some preliminary analysis on the two tables that we’ll focus on. For this, we will select \u003Cstrong\u003EWorksheets\u003C/strong\u003E under \u003Cstrong\u003EProjects\u003C/strong\u003E in the top left corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i10_start.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EData Problem\u003C/h3\u003E\n","\u003Cp\u003ESometimes you go through the entire process of building a predictive model and the predictions are quite poor and you trace the issue back to data problems.  In other cases, such as this one, the data changes with time and the models go bad.\u003C/p\u003E\n","\u003Ch3\u003EPreparing the Data for Further Data Analysis and Consumption\u003C/h3\u003E\n","\u003Ch4\u003EStep 1 - Create Schema and Tables\u003C/h4\u003E\n","\u003Cp\u003ENow let's create the datastructuresa into which we are going to load the data. We will be using the database that was created when connecting to Dataiku - \u003Ccode\u003EPC_DATAIKU_DB\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003ECopy the statements below into your worksheet and run them there.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E   use warehouse PC_DATAIKU_WH;\n   use database PC_DATAIKU_DB; \n   create or replace schema RAW; \n   use schema RAW;\n\n   create or replace table EARNINGS_BY_EDUCATION (\n     EDUCATION_LEVEL varchar(100),\n     MEDIAN_WEEKLY_EARNINGS_USD decimal(10,2) \n   );\n\n   create or replace table JOB_POSTINGS (\n     JOB_ID int,\n     TITLE varchar(200),\n     LOCATION varchar(200),\n     DEPARTMENT varchar(200),\n     SALARY_RANGE varchar(20),\n     COMPANY_PROFILE varchar(20000),\n     DESCRIPTION varchar(20000),\n     REQUIREMENTS varchar(20000),\n     BENEFITS varchar(20000),\n     TELECOMMUNTING int,\n     HAS_COMPANY_LOGO int,\n     HAS_QUESTIONS int,\n     EMPLOYMENT_TYPE varchar(200),\n     REQUIRED_EXPERIENCE varchar(200),\n     REQUIRED_EDUCATION varchar(200),\n     INDUSTRY varchar(200),\n     FUNCTION varchar(200),\n     FRAUDULENT int\n   );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2 - Load Data\u003C/h4\u003E\n","\u003Cp\u003EThe data we want to use is available as csv files. Hence we define a csv file format to make our lives easier\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace file format csvformat\ntype = csv\nfield_delimiter =','\nfield_optionally_enclosed_by = '"', \nskip_header=1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs we have stored the data we want to load on an external S3 bucket, we need to create an external stage to load that data and also a stage for Dataiku to push Snowpark UDFs to.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE JOB_DATA\n  file_format = csvformat\n  url='s3://dataiku-snowflake-labs/data';\n\nCREATE or REPLACE STAGE DATAIKU_DEFAULT_STAGE;\n  \n ---- List the files in the stage \n\n list @JOB_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith that all set, we are ready to load the data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecopy into EARNINGS_BY_EDUCATION \nfrom @JOB_DATA/earnings_by_education.csv\non_error='continue';\n\ncopy into JOB_POSTINGS\nfrom @JOB_DATA/job_postings.csv\non_error='continue';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's a quick look at the data\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from RAW.EARNINGS_BY_EDUCATION limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_4_query_education.png\" alt=\"4\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from RAW.JOB_POSTINGS limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_5_query_postings.png\" alt=\"5\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3 - Prepare Data for Analytics with Dataiku\u003C/h4\u003E\n","\u003Cp\u003EWith the data loaded into our \u003Ccode\u003Eraw\u003C/code\u003E stage, we want to prepare a table that joins the two sources into one, which we will then use in our workflow in Dataiku.\u003C/p\u003E\n","\u003Cp\u003ELet's start by switching to the Public schema as the Dataiku connection created from Partner Connect has permissions on that.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd now on to the new table\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace table JOBS_POSTINGS_JOINED as\nselect \n    j.JOB_ID as JOB_ID,\n    j.TITLE as TITLE,\n    j.LOCATION as LOCATION,\n    j.DEPARTMENT as DEPARTMENT,\n    j.SALARY_RANGE as SALARY_RANGE,\n    e.MEDIAN_WEEKLY_EARNINGS_USD as MEDIAN_WEEKLY_EARNINGS_USD,\n    j.COMPANY_PROFILE as COMPANY_PROFILE,\n    j.DESCRIPTION as DESCRIPTION,\n    j.REQUIREMENTS as REQUIREMENTS,\n    j.BENEFITS as BENEFITS,\n    j.TELECOMMUNTING as TELECOMMUTING,\n    j.HAS_COMPANY_LOGO as HAS_COMPANY_LOGO,\n    j.HAS_QUESTIONS as HAS_QUESTIONS,\n    j.EMPLOYMENT_TYPE as EMPLOYMENT_TYPE,\n    j.REQUIRED_EXPERIENCE as REQUIRED_EXPERIENCE,\n    j.REQUIRED_EDUCATION as REQUIRED_EDUCATION,\n    j.INDUSTRY as INDUSTRY,\n    j.FUNCTION as FUNCTION,\n    j.FRAUDULENT as FRAUDULENT\nfrom RAW.JOB_POSTINGS j left join RAW.EARNINGS_BY_EDUCATION e on j.REQUIRED_EDUCATION = e.EDUCATION_LEVEL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour data should now look like this\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from PUBLIC.JOB_POSTINGS_JOINED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_6_query_postings_enriched.png\" alt=\"6\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 4 - Grant Dataiku Access to Data\u003C/h4\u003E\n","\u003Cp\u003EAs a last step before heading over to Dataiku, we need to make sure that it can read the data we just loaded and joined. (Note: You wouldn't typically grant ALL like this but we are in isolated trial accounts)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Egrant ALL on all schemas in database PC_DATAIKU_DB to role PC_Dataiku_role;\ngrant ALL privileges on database PC_DATAIKU_DB to role PC_Dataiku_role;\ngrant ALL on all stages in database PC_DATAIKU_DB to role PC_Dataiku_role;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Compute vs Other Warehouses\u003C/strong\u003E <br> Many of the warehouse/compute capabilities we just covered, like being able to create, scale up and out, and auto-suspend/resume warehouses are things that are simple in Snowflake and can be done in seconds. Yet for on-premise data warehouses these capabilities are very difficult (or impossible) to do as they require significant physical hardware, over-provisioning of hardware for workload spikes, significant configuration work, and more challenges. Even other cloud data warehouses cannot scale up and down like Snowflake without significantly more configuration work and time.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWarning - Watch Your Spend!\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EDuring or after this lab you should \u003Cem\u003ENOT\u003C/em\u003E do the following without good reason or you may burn through your $400 of free credits more quickly than desired:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDisable auto-suspend. If auto-suspend is disabled, your warehouses will continue to run and consume credits even when not being utilized.\u003C/li\u003E\u003Cli\u003EUse a warehouse size that is excessive given the workload. The larger the warehouse, the more credits are consumed.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe are going to use the virtual warehouse \u003Ccode\u003EPC_DATAIKU_WH\u003C/code\u003E for our Dataiku work. However, we are first going to slightly increase the size of the warehouse to increase the compute power it contains.\u003C/p\u003E\n","\u003Cp\u003EOn the top right corner of your worksheet, click on the warehouse name. In the dialog, click on the three lines on the top right to get to the details page of the warehouses. There, change the size of the \u003Ccode\u003EPC_DATAIKU_WH\u003C/code\u003E data warehouse from X-Small to Medium. Then click the “Finish” button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_d_warehouse.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAlternatively, you can also run the following command in the worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter warehouse PC_DATAIKU_WH set warehouse_size=MEDIUM;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating And Running A Dataiku Project\u003C/h2\u003E\n","\u003Cp\u003EFor this module, we will login into the Dataiku hosted trial account and create a Dataiku project.\u003C/p\u003E\n","\u003Cp\u003EHere is the project we are going to build along with some annotations to help you understand some key concepts in Dataiku.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Final_Flow2.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Edataset\u003C/strong\u003E is represented by a blue square with a symbol that depicts the dataset type or connection. The initial datasets (also known as input datasets) are found on the left of the Flow. In this project, the input dataset will be the one we created in the first part of the lab.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Erecipe\u003C/strong\u003E in Dataiku DSS (represented by a circle icon with a symbol that depicts its function) can be either visual or code-based, and it contains the processing logic for transforming datasets.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMachine learning processes\u003C/strong\u003E are represented by green icons.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EActions Menu\u003C/strong\u003E is shown on the right pane and is context sensitive.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhatever screen you are currently in you can always return to the main \u003Cstrong\u003EFlow\u003C/strong\u003E by clicking the \u003Cstrong\u003EFlow\u003C/strong\u003E symbol from the top menu (also clicking the project name will take you back to the main Project page).\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou can refer back to this completed project screenshot if you want to check your progress through the lab. (Note though that if you choose to use the\nSnowparkML plugin your final flow will look a little different)\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E If you didn't setup AI Assistants from the Extensions menu in the earlier Partner Connect lab do it now.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreating a Dataiku Project\u003C/h3\u003E\n","\u003Cp\u003EGo back to your Dataiku Cloud instance landing page.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEnsure you are on the \u003Ccode\u003EOverview\u003C/code\u003E page\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003EOPEN INSTANCE\u003C/code\u003E to get started.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Proj_1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations you are now using the Dataiku platform! For the remainder of this lab we will be working from this environment which is called the \u003Ccode\u003Edesign node\u003C/code\u003E, its the pre-production environment where teams collaborate to build data products.\u003C/p\u003E\n","\u003Cp\u003ENow lets create our first project. There are lots of existing options and accelerators available to us but for this lab we will start with a blank project.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on the \u003Ccode\u003E+ NEW Project\u003C/code\u003E button on the right hand side\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EBlank Project\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EGive your project a name such as \u003Ccode\u003EJobs Fraud\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Ccode\u003ECreate\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Proj_2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESuccess! You’ve now created a dataiku project.\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003EGot it!\u003C/code\u003E to minimize the pop-up on \u003Ccode\u003ENavigation and help in DSS\u003C/code\u003E and return to the project home screen.\u003C/p\u003E\n","\u003Cp\u003EReview the Dataiku DSS page. There are a few things to note from the project landing page on an example project:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EThe project name, image associated with the project, collaborators, and optional tags:\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe number and types of objects in the project.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA description of the project written in markdown, can link specific Dataiku objects (e.g., datasets, saved models, etc.) in the description:\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESummary of project (history is saved in a git log) as well as a Chat function for better collaboration:\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EImport Datasets\u003C/h3\u003E\n","\u003Cp\u003EImport the dataset from Snowflake\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003E+IMPORT YOUR FIRST DATASET\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EUnder SQL, select \u003Ccode\u003ESnowflake\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/dataiku64.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003E1.To load the table, select the connection that was just created for us from \u003Ccode\u003EPartner Connect\u003C/code\u003E. In the Table section select \u003Ccode\u003EGet Tables List\u003C/code\u003E. Dataiku will warn you that this may be long list but we can OK this.\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003ESearch for and select the \u003Ccode\u003EJOBS_POSTINGS_JOINED\u003C/code\u003E table we just created in Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThen click \u003Ccode\u003ETEST TABLE\u003C/code\u003E to test the connection\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf successful set the \u003Ccode\u003ENew dataset name\u003C/code\u003E (top right) to \u003Ccode\u003EJOBS_POSTINGS_JOINED\u003C/code\u003E and click on \u003Ccode\u003ECREATE\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_CON1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EReturn to the flow by clicking on the \u003Ccode\u003Eflow\u003C/code\u003E icon in the top left \u003Cem\u003E(keyboard shortcut g+f)\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Flow1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDouble click on the \u003Ccode\u003EJOBS_POSTINGS_JOINED\u003C/code\u003E dataset\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EJOBS_POSTINGS_JOINED\u003C/code\u003E table contains data on a location and day basis about the number and types of cases (Active, Confirmed, Deaths, Recovered) that day.\u003C/p\u003E\n","\u003Cp\u003EDataiku reads a sample of 10000 rows by default. The sampling method can be changed under \u003Ccode\u003EConfigure Sample\u003C/code\u003E but for this lab we can leave it as the default:\u003C/p\u003E\n","\u003Cp\u003EDataiku automatically detects data type and meaning of each column. The status bar shows how much of the data is valid (green), invalid (red), and missing (grey). You can view column Stats (data quality, distributions) by clicking \u003Ccode\u003EQuick Column Stats\u003C/code\u003E button on the right:\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Ccode\u003Eclose\u003C/code\u003E button when you are finished\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleaning The Data With The Prepare Recipe\u003C/h2\u003E\n","\u003Cp\u003EAfter exploring our data we are going to perform some transformation steps to clean the data and generate new features.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThere are two really important concepts happening in this lab:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFirstly\u003C/strong\u003E The data stays in Snowflake. We work on a configurable sample of the data in memory, our dataset is quite small but it might not be\nand by working on a sample in memory we avoid unnecessary movement of data out of Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESecondly\u003C/strong\u003E When you run the transformations you build in this section you may notice beneath the \u003Ccode\u003ERUN\u003C/code\u003E button Dataiku specified the engine as \u003Ccode\u003EIn-database\u003C/code\u003E.\nDataiku will always try to use the most efficient engine for any job and in this case it sees we\nare working on Snowflake data and will therefore push down to the Snowflake Virtual Warehouse\nthat was created when we set up through Partner Connect.\u003C/p\u003E\n","\u003Cp\u003EThe ability of Dataiku to minimise data movement and push the code to where the data lives gives great benefits in terms of performance, costs and governance.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EDataiku terms these transformation steps as \u003Ccode\u003ERecipes\u003C/code\u003E and they may be visual (UI) or code based (a variety of editors, notebooks and IDE's are available).\u003C/p\u003E\n","\u003Cp\u003ELets start with a visual recipe called the \u003Ccode\u003EPrepare\u003C/code\u003E recipe. You can think of this recipe like a toolbox with lots of different tools for a variety of data transformation tasks. You build a series of transformation steps and check their effect on a sample of the data before pushing them to the full dataset.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect your dataset from the flow (remember you can use the \u003Ccode\u003Eg+f\u003C/code\u003E keyboard shortcut)\u003C/li\u003E\u003Cli\u003EAfter highlighting the dataset by clicking on it once go to the right hand actions menu select the \u003Ccode\u003EPrepare\u003C/code\u003E recipe from the Visual Recipes list\u003C/li\u003E\u003Cli\u003EYou can leave the defaults and click on \u003Ccode\u003ECREATE RECIPE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prepare1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELocation Column\u003C/h3\u003E\n","\u003Cp\u003ELooking at our data we can see the location column has a lot of information contained within it that could make useful features for our model however in its current comma separated string format it is not that useful. Lets use the \u003Ccode\u003ESplit\u003C/code\u003E processor to pull out the location information into their own columns.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on the \u003Ccode\u003E+ ADD A NEW STEP\u003C/code\u003E button on the left\u003C/li\u003E\u003Cli\u003EYou can use the search window to find the split processor\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003ESplit Column\u003C/code\u003E processor.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_Split1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA new step is added to script on the left. We now need to populate the fields so Dataiku knows how we'd like to apply the split.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFor the column we want to enter \u003Ccode\u003Elocation\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIt's comma separated so the delimiter will be \u003Ccode\u003E,\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EWe can leave the prefix as the default\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003ETruncate\u003C/code\u003E option\u003C/li\u003E\u003Cli\u003ESince there are three comma separated location values change the columns to keep to \u003Ccode\u003E3\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EAs you fill in the values you can see the effects live in the blue columns which is a great way of understanding the impact of the changes you are making and if it is the desired outcome.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_Split2.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn addition to \u003Ccode\u003Eg+f\u003C/code\u003E one of the other most useful keyboard shortcuts is \u003Ccode\u003Ec\u003C/code\u003E when your are in the Explore tab. This allows you to search and scroll to a\nparticular column. Very useful for wider datasets.\nTake a look at the \u003Ca href=\"https://doc.dataiku.com/dss/latest/accessibility/index.html\"\u003EDocumentation\u003C/a\u003E for more.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESplitting the column was useful but lets make the column names a little more human readable. We can use the rename processor for this. Select the \u003Ccode\u003ERename\u003C/code\u003E processor just like you did for Split and then click on \u003Ccode\u003E+Add Renaming\u003C/code\u003E and rename location_0 to country. Repeat for location_1 and location_2 changing them to state and city respectively. The step should look like this\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_rename.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou could also achieve this by right clicking on the column name and selecting \u003Ccode\u003Erename\u003C/code\u003E.\nWhen you right click on a column Dataiku makes suggestions on the most common transformations based\non the type of data in the column.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EText Columns\u003C/h3\u003E\n","\u003Cp\u003ENext we have a number of text columns. When building a machine learning model there are a number of techniques we can use to work with text data, we are going to simplify the text and use the Normalise feature which transforms to lowercase, removes punctuation and accents and performs Unicode NFD normalization (Café -> cafe).\u003C/p\u003E\n","\u003Cp\u003EWe could search for the processor we want and configure it like before but since we are new to Dataiku lets use the AI Prepare assistant to help us out this time. We can describe the steps we want and allow the AI Assistant to look through the 100+ processors and configure them to our requirements.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EAI PREPARE\u003C/code\u003E button on the left side of the screen\u003C/li\u003E\u003Cli\u003EIn the text box paste in the following prompt and then click on \u003Ccode\u003EGENERATE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Enormalize text for the columns COMPANY_PROFILE, DESCRIPTION, REQUIREMENTS, BENEFITS. \ndont create a new column, update in place\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI Assitant generates the 4 steps for us and documents them to make the results are easy to review for everyone using the data preparation job\u003C/p\u003E\n","\u003Cp\u003ENow we have normalized the text in those columns we might consider creating a new feature based on the length. Our theory might be that scammers will focus on the salary and buzzwords to get people to apply and are less likely to populate the job description and company background.\u003C/p\u003E\n","\u003Cp\u003EAgain if we know the processor we want we can just search and use it directly. In our case as we're new to Dataiku let's use the AI Prepare assistant to help us out.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EAI PREPARE\u003C/code\u003E button on the left side of the screen\u003C/li\u003E\u003Cli\u003EIn the text box paste in the following prompt and then click on \u003Ccode\u003EGENERATE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecalculate the length of the columns COMPANY_PROFILE, DESCRIPTION, REQUIREMENTS, BENEFITS.\nwrite them to new columns with the prefix LENGTH_\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf your script now matches the below screenshot go ahead and click on the green \u003Ccode\u003ERUN\u003C/code\u003E button at the bottom of the script.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prepare_finish.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EUsing AI Assistants in this way can be a very powerful tool but it is important to review the generated steps to ensure that it achieves\nyour aims accurately.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Engineering With Snowpark\u003C/h2\u003E\n","\u003Cp\u003EIn addition to a wide number of visual tools to enable to the low/no coder Dataiku also provides rich and familiar toolsets and language support for coders.\u003C/p\u003E\n","\u003Cp\u003EIn this section we will put ourselves in the shoes of a data scientist that is collaborating on the project. Whilst they can get value from tools like the Prepare recipe they may be looking for full code experience so in this section we will use the built-in support in Dataiku for notebooks and IDE's\u003C/p\u003E\n","\u003Cp\u003ELets use a Jupyter notebook to create a Snowpark for Python function to extract the minimum salary range\u003C/p\u003E\n","\u003Cp\u003EWhen using Dataiku's SaaS option from Partner Connect the setup is done for us automatically and we checked that in our earlier lab where we set up the AI Services. If for any reason you skipped that step earlier then return to your browser tab with \u003Ccode\u003EDataiku Launchpad\u003C/code\u003E open (if you have shut this just go to \u003Ca href=\"https://launchpad-dku.app.dataiku.io/\"\u003ELaunchpad\u003C/a\u003E and check that \u003Ccode\u003ESnowpark\u003C/code\u003E is enabled under the \u003Ccode\u003EExtensions\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESnowpark code\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIntegrations:\u003C/strong\u003E Much like in our last chapter here we are using Dataiku's deep integrations with Snowflake to work on data in the most efficient way. Our data scientist can\nuse the tools they are most familiar with in Dataiku whilst also collaborating on the project with non-coding colleagues and even packaging custom code-based functions in a visual interface to expose complex tasks to less technical users. The data is loaded into a\nSnowpark Python DataFrame and when we execute our code we push the computation to Snowpark.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ELets create our Python code recipe:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the flow select the output dataset from our prepare recipe and then from the actions menu on the right select \u003Ccode\u003EPython\u003C/code\u003E from the code recipes section.\u003C/li\u003E\u003Cli\u003EIn the \u003Ccode\u003EOutputs\u003C/code\u003E section click \u003Ccode\u003E+ ADD\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ELet's name our new output dataset \u003Ccode\u003EJobs_Python\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECreate Dataset\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE RECIPE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark1a.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to set a code environment that has the correct packages in. Fortunately that has been created for us, we just need to select it for this recipe.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on the \u003Ccode\u003EAdvanced\u003C/code\u003E tab at the top of the screen\u003C/li\u003E\u003Cli\u003EUnder the Python Env. section change the \u003Ccode\u003ESelection behaviour\u003C/code\u003E to \u003Ccode\u003ESelect an environment\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIn the \u003Ccode\u003EEnvironment\u003C/code\u003E drop down select the \u003Ccode\u003Esnowpark\u003C/code\u003E code environment.\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ESave\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003ECode\u003C/code\u003E tab to return to the main editor\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark_1b.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EA Note on Code Environments:\u003C/strong\u003E  Dataiku uses the concept of code environments to address the problem of managing dependencies and\nversions when writing code in R and Python. Code environments provide a number of benefits such as \u003Cstrong\u003EIsolation and Reproducibility\u003C/strong\u003E of\nresults. When using Snowpark for Python from Dataiku you will use a code environment that includes the Snowpark library as well as other\npackages you wish to use. In our lab, to make things easy, we are using a default Snowpark code environment which just contains just the\nminimum required libraries but once you have completed the lab and wish to explore further you can create your own code environments.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn addition to selecting an appropriate code environment there are just a couple of extra lines of code to add to your DSS recipe to start using Snowpark for Python\u003C/p\u003E\n","\u003Cp\u003EHelpfully we can use one of the many code samples available to us.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDelete the automatically generated python starter code\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003E{CODE SAMPLES}\u003C/code\u003E button\u003C/li\u003E\u003Cli\u003ESearch for Snowpark\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003ERead and write datasets with Snowpark\u003C/code\u003E option\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003E+ INSERT\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark2a.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou now have some starter Snowpark code with the correct input and output dataset names.\u003C/p\u003E\n","\u003Cp\u003EWe could carry on using the default code editor if we wish but we also have the option to use notebooks or IDE's so lets go ahead and use the in-built Jupyter notebook for the next part.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Ccode\u003EEDIT IN NOTEBOOK\u003C/code\u003E option in the top-right. We are going to use the Dataiku package and some Snowpark functions so lets add that now and feel free to separate into cells if you wish. Add the following two lines at the start of your code:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E#add these two lines at the start of your code\nimport dataiku\nfrom snowflake.snowpark.functions import *\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow lets take a simple example of feature engineering in code. \u003Ccode\u003EDelete\u003C/code\u003E the section that reads:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# TODO: Replace this part by your actual code that computes the output, as a Snowpark dataframe\n# For this sample code, simply copy input to output\noutput_dataset_df = input_dataset_df\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELets replace that deleted section with our Snowpark for Python code to generate a new feature called \u003Ccode\u003Emin_salary\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E#strip minimum salary from the given range\noutput_dataset_df = input_dataset_df.withColumn('"MIN_SALARY"', split(col('"SALARY_RANGE"'), lit('-'))[0])\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EOf course this is a very simple piece of feature engineering and our data scientist could go much further but it demonstrates how our code first users can easily work alongside their colleagues\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYour code should now look similar to this (don't worry if you haven't separated your code into cells)\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark3.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ERun your cell(s) to make sure your code is correct\u003C/li\u003E\u003Cli\u003EClick the \u003Ccode\u003ESAVE BACK TO RECIPE\u003C/code\u003E button near the top of the screen\u003C/li\u003E\u003Cli\u003EFrom the default code editor click \u003Ccode\u003ERUN\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESplit The Dataset\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFor the remainder of this lab we will be using Dataiku's Visual ML interface to design, train & test our model. This is the best option for most circumstances, however if you are specifically interested in trying out Snowflakes SnowparkML library then you could, of course, write that code from Dataiku as we just saw with the Python recipe but, even better, Dataiku provides a UI via a plugin so non-coders can use it. If you wish to develop your model using that plugin then jump to the optional chapter on the SnowparkML plugin near the end of this guide\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EOne advantage of an end-to-end platform like Dataiku is that data preparation can be done in the same tool as machine learning. For example, before building a model, you may wish to create a holdout set. Let’s do this with a visual recipe.\u003C/p\u003E\n","\u003Ch3\u003ESteps\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EFrom the Flow, click the \u003Ccode\u003EJobs_Python\u003C/code\u003E dataset once to select it.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen the Actions tab on the right.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the Split recipe from the menu of visual recipes.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003E+ Add\u003C/code\u003E; name the output \u003Ccode\u003Etrain\u003C/code\u003E; and click Create Dataset.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003E+ Add\u003C/code\u003E again; name the second output \u003Ccode\u003Etest\u003C/code\u003E; and click Create Dataset.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOnce you have defined both output datasets, click \u003Ccode\u003ECreate Recipe\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Split1a.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EDefine a split method\u003C/h3\u003E\n","\u003Cp\u003EOn the Splitting step of the recipe, choose \u003Ccode\u003EDispatch percentiles of sorted data\u003C/code\u003E as the splitting method.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESet to sort according to \u003Ccode\u003EJOB_ID\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESet the ratio of 80 % to the \u003Ccode\u003Etrain\u003C/code\u003E dataset, and the remaining 20% to the \u003Ccode\u003Etest\u003C/code\u003E dataset.\u003C/li\u003E\u003Cli\u003EClick the green Run at the bottom left to build these two output datasets.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Split2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen the job finishes, navigate back to the Flow (g + f) to see your progress.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETrain A Model\u003C/h2\u003E\n","\u003Cp\u003EThe first step is to define the basic parameters of the machine learning task at hand.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the train dataset.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the Actions tab, click on the Lab button. Alternatively, navigate to the Lab tab of the right side panel (shown below).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAmong the menu of visual ML tasks, choose AutoML Prediction.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Train1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow you just need to choose the target variable and which kind of models you want to build.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EChoose \u003Ccode\u003EFRAUDULENT\u003C/code\u003E as the target variable on which to create the prediction model.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Create, keeping the default setting of Quick Prototypes.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_2.png\" alt=\"64\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETrain models with the default design\u003C/h3\u003E\n","\u003Cp\u003EBased on the characteristics of the input training data, Dataiku has automatically prepared the design of the model. But no models have been trained yet!\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EBefore adjusting the design, click Train to start a model training session.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Train again to confirm.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_3.png\" alt=\"64\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInspect The Results\u003C/h2\u003E\n","\u003Cp\u003EOnce your models have finished training, let’s see how Dataiku did.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWhile in the Result tab, click on the Random forest model in Session 1 on the left hand side of the screen to open a detailed model report.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECheck Model Explainability - Feature Importance\u003C/h3\u003E\n","\u003Cp\u003EOne important aspect of a model is the ability to understand its predictions. The Explainability section of the report includes many tools for doing so.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Explainability section on the left, click to open the \u003Ccode\u003EFeature importance\u003C/code\u003E panel to see an estimate of the influence of a feature on the predictions.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECheck Model Explainability - Confusion Matrix\u003C/h3\u003E\n","\u003Cp\u003EA useful tool to evaluate and compare classification models is the confusion matrix. This compares the actual values of the target variable to our models predictions broken down into where the model got it right (true positives & true negatives) and where it got it wrong (false positives & false negatives).\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Performance section on the left, click to open the \u003Ccode\u003EConfusion Matrix\u003C/code\u003E panel\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect3.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECheck Model Explainability - What If?\u003C/h3\u003E\n","\u003Cp\u003EWhat if analyses can be a useful exercise to help both data scientists and business analysts get a sense for what a model will predict, given different input values. You can use the drop-down menus and sliders to adjust the values, type in your own, or even choose to ignore features to simulate a situation with missing values. On the right, you can review the new prediction based on your inputs.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick on the \u003Ccode\u003EWhat If?\u003C/code\u003E to open the panel.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect4.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECheck Model Information\u003C/h3\u003E\n","\u003Cp\u003EAlongside the results, you’ll also want to be sure how exactly the model was trained.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the Model Information section, click to open the Features panel to check which features were included in the model, which were rejected (such as the text features), and how they were handled.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen finished, at the top of the model report, click on Models to return to the Result home.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_info.png\" alt=\"65\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThere are many more features to better understand your model. Feel free to explore them as time permits\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIterate On The Model Training Design (optional)\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis chapter is optional in the lab for timing reasons but would be a standard part of real world model development.\nFeel free to cover it now if you have time or return to it later to improve your model\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThus far, Dataiku has produced quick prototypes. From these baseline models, you can work on iteratively adjusting the design, training new sessions of models, and evaluating the results.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESwitch to the \u003Ccode\u003EDesign\u003C/code\u003E tab at the top center of the screen.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETour the Design tab\u003C/h3\u003E\n","\u003Cp\u003EFrom the Design tab, you have full control over the design of a model training session. Take a quick tour of the available options. Some examples include:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003ETrain / Test Set\u003C/code\u003E panel, you could apply a k-fold cross validation strategy.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EFeature reduction\u003C/code\u003E panel, you could apply a reduction method like Principal Component Analysis.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EAlgorithms\u003C/code\u003E panel, you could select different machine learning algorithms or import custom Python models.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EReduce the number of features\u003C/h3\u003E\n","\u003Cp\u003EInstead of adding complexity, let’s simplify the model by including only the most important features. Having fewer features could hurt the model’s predictive performance, but it may bring other benefits, such as greater interpretability, faster training times, and reduced maintenance costs.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EDesign\u003C/code\u003E tab, navigate to the \u003Ccode\u003EFeatures handling\u003C/code\u003E panel on the left.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick the box at the top left of the feature list to select all.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor the role, click \u003Ccode\u003EReject\u003C/code\u003E to de-select all features.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETurn on the three most influential features according to the Feature importance chart seen earlier: \u003Ccode\u003ECOUNTRY, HAS_COMPANY_LOGO, LENGTH_COMPANY_PROFILE\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate3.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYour top three features may be slightly different. Feel free to choose these three or the three most important from your own results.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETrain a second session\u003C/h3\u003E\n","\u003Cp\u003EOnce you have just the top three features in the model design, you can kick off another training session.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the blue \u003Ccode\u003ETrain\u003C/code\u003E button near the top right to start the next session.\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ETrain\u003C/code\u003E once more to confirm.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate4.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn reality our results from both training runs are suspiciously high and would merit further investigation. Indeed if you click on the diagnostics that Dataiku helpfully runs for each training session you can see a warning for an imbalanced dataset. If you switch the metric to \u003Ccode\u003EF1\u003C/code\u003E (which is a better metric for imbalanced datasets) you will see a significant drop in score. There are many ways Dataiku can help, for example with the \u003Ccode\u003Eclass rebalancing\u003C/code\u003E sampling method. It is beyond the scope of this course but read up in our\ndocumentation or blogs or take one of the more advanced Dataiku Academy ML courses to understand how Dataiku ML Diagnostics can help you identify and troubleshoot potential issues and suggest possible improvements as you build your model.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EApply A Model To Generate Predictions On New Data\u003C/h2\u003E\n","\u003Cp\u003EUp until now, the models you’ve trained are present only in the Lab, a space for experimental prototyping and analysis. You can’t actually use any of these models until you have added them to the Flow, where your actual project pipeline of datasets and recipes lives. Let’s do that now!\u003C/p\u003E\n","\u003Ch3\u003EChoose a model to deploy\u003C/h3\u003E\n","\u003Cp\u003EMany factors could impact the choice of which model to deploy. For many use cases, the model’s performance is not the only deciding factor.\u003C/p\u003E\n","\u003Cp\u003ECompared to the larger model, the simple model with three features cost about 4 hundredths of a point in performance. For some use cases, this may be a huge amount, but in others it may be a bargain for a model that is more interpretable, cheaper to train, and easier to maintain. Since performance is not too important in this tutorial, let’s choose the simpler option.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Result tab, click the Random forest (s2) to open the model report of the simpler random forest model from Session 2.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow you just need to deploy this model from the Lab to the Flow.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Ccode\u003EDeploy\u003C/code\u003E near the top right.\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECreate\u003C/code\u003E to confirm.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EScore Data\u003C/h3\u003E\n","\u003Cp\u003EYou now have two green objects in the Flow that you can use to generate predictions on new data: a training recipe and a saved model object.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Flow single click on the diamond-shaped saved model to select it\u003C/li\u003E\u003Cli\u003EFrom the Actions menu select the \u003Ccode\u003EScore\u003C/code\u003E recipe\u003C/li\u003E\u003Cli\u003EFor the \u003Ccode\u003EInput Dataset\u003C/code\u003E select the \u003Ccode\u003Etest\u003C/code\u003E dataset\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ECREATE RECIPE\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score3a.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Score recipe you can leave the defaults but make sure that Snowflake Java UDF is selected as the engine. If it isn't click on the gear cog and select it. When you are done click \u003Ccode\u003ERUN\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score4.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou may notice the \u003Ccode\u003E..Java UDF\u003C/code\u003E part of that engine. This is one of a number of places that Dataiku embeds Snowpark Java UDFs into the product for the\nbest integration and performance. From your perspective as a user Dataiku will take care of the details and it simply means the task at hand runs faster\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EInspect the scored data\u003C/h3\u003E\n","\u003Cp\u003ECompare the schemas of the test and test_scored datasets.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWhen the job finishes, click Explore dataset \u003Ccode\u003Etest_scored\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EScroll to the right, and note the addition of three new columns: \u003Ccode\u003Eproba_0, proba_1, and prediction\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ENavigate back to the Flow to see the scored dataset in the pipeline.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score6.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EHow well was the model able to identify the fake job postings in the test dataset? That is a task for the Evaluate recipe, which you will encounter in other learning resources.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDocument The Flow (optional)\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis chapter is optional in the lab for timing reasons but documenting your project along with other capabilities in Dataiku like automatic generation of model documentation is important in MLOps)\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EDataiku can generate explanations of project Flows. The feature leverages a Large Language Model (LLM) to do this.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOn the Flow screen open the Flow Actions menu\u003C/li\u003E\u003Cli\u003ESelect Explain Flow\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIt is possible to adjust the generated explanations for language, purpose and length. Apply the following and then set the generated text as the project description.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELanguage: English\u003C/li\u003E\u003Cli\u003EPurpose: Business\u003C/li\u003E\u003Cli\u003ELength: Medium\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Explainflow_1.png\" alt=\"64\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUsing Snowpark ML Plugin (optional)\u003C/h2\u003E\n","\u003Cp\u003ESnowflake recently released a collection of python APIs enabling efficient ML model development directly in Snowflake. You can, of course, use this library directly from Dataiku in a code recipe but we also provide a free to use plugin to provide a UI.\u003C/p\u003E\n","\u003Cp\u003EThere are a few steps you need to take to install the plugin and prepare the data.\u003C/p\u003E\n","\u003Ch3\u003EInstall the plugin\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EReturn the Dataiku Cloud launchpad (https://launchpad-dku.app.dataiku.io)\u003C/li\u003E\u003Cli\u003EIn the \u003Ccode\u003EPlugins\u003C/code\u003E section select \u003Ccode\u003E+ ADD A PLUGIN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESearch for and install the Visual SnowparkML plugin\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EData pre-processing\u003C/h3\u003E\n","\u003Cp\u003EWhen using the plugin there are a few additional pre-processing steps necessary that we don't need to do when using Dataiku's standard Visual ML interface. Firstly we would need to make sure that all the column names are in uppercase but fortunately in our dataset that is already the case. Secondly we need to make sure that any columns of type \u003Ccode\u003Eint\u003C/code\u003E that have missing values are converted to \u003Ccode\u003Edoubles\u003C/code\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick once on the \u003Ccode\u003EJobs_Python\u003C/code\u003E dataset in the flow to select it and then choose the \u003Ccode\u003EPrepare\u003C/code\u003E recipe from the Actions menu, just like we did earlier in the lab\u003C/li\u003E\u003Cli\u003EThere are a number of columns of type \u003Ccode\u003Eint\u003C/code\u003E with missing values. Change these to doubles by clicking on the datatype under the column name and selecting it.\u003C/li\u003E\u003Cli\u003EClick \u003Ccode\u003ERUN\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESnowparkML Plugin\u003C/h3\u003E\n","\u003Cp\u003ENow we have performed our preprocessing select the output dataset and then the plugin from the \u003Ccode\u003EActions\u003C/code\u003E menu (Note: You may need to scroll down to find the plugins, they are below the code and LLM recipes)\u003C/p\u003E\n","\u003Cp\u003EThere are a number of output fields to fill:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESet an output dataset name for the \u003Ccode\u003ETrain Dataset Output\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESet an output dataset name for the \u003Ccode\u003ETest Dataset Output\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESet a name for \u003Ccode\u003EModel Folder\u003C/code\u003E where the MLflow experiment tracking data and trained models will be stored\u003C/li\u003E\u003Cli\u003EOptionally you can set a folder for the final best model but we can leave this blank\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowml1.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we can set the details of our training run.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGive the final model a name\u003C/li\u003E\u003Cli\u003ESet the target column to \u003Ccode\u003EFraudulent\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThis is a \u003Ccode\u003ETwo-class classification\u003C/code\u003E problem\u003C/li\u003E\u003Cli\u003EThe ratio can be set to \u003Ccode\u003E0.8\u003C/code\u003E for the standard 80/20 split and a random seed can also be set.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou can now set your Metrics, Features, Algos and more for your training session. Just click \u003Ccode\u003ERUN\u003C/code\u003E at the bottom left when you are happy with your setup\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowml2.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations. You are using SnowparkML from a UI! You can explore your model from the \u003Ccode\u003EMLflow\u003C/code\u003E green diamond in the \u003Ccode\u003EFlow\u003C/code\u003E looking at explainability and performance measures, model comparisons and much more.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusions And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this introductory lab exercise! Congratulations! You've mastered the Snowflake basics and you’ve taken your first steps toward data cleansing, feature engineering and training machine learning models with Dataiku.\u003C/p\u003E\n","\u003Cp\u003EYou have seen how Dataiku's deep integrations with Snowflake can allow teams with different skill sets get the most out of their data at every stage of the machine learning lifecycle.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue with your free trial and continue to refine your models and by using some of the more advanced capabilities not covered in this lab.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create stages, databases, tables, views, and virtual warehouses.\u003C/li\u003E\u003Cli\u003EHow to load structured and semi-structured data.\u003C/li\u003E\u003Cli\u003EHow to perform analytical queries on data in Snowflake, including joins between tables.\u003C/li\u003E\u003Cli\u003EHow to create a Dataiku trial account through Partner Connect\u003C/li\u003E\u003Cli\u003EHow to use both Visual and Code Recipes to explore and transform data\u003C/li\u003E\u003Cli\u003EHow to train, explore and understand a machine learning model\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.snowflake.com/s/\"\u003ESnowflake Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.dataiku.com/\"\u003EDataiku Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESign up for \u003Ca href=\"http://https://community.snowflake.com/s/snowflake-university\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://academy.dataiku.com/\"\u003EDataiku Academy\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n\n\nThis Snowflake Quickstart introduces you to the basics of using Snowflake together with Dataiku Cloud as part of a Data Science project. We’ll be highlighting some of the well-integrated functionalities between the two technologies. It is designed specifically for use with the [Snowflake free 30-day trial](https://trial.snowflake.com), and the Dataiku Cloud free trial version via Snowflake’s Partner Connect. \n\n**The Use Case:** Recent advances in generative AI have made it easy to apply for jobs. But be careful! Scammers have also been known to create fake job applications in the hopes of stealing personal information. Let’s see if you — with Dataiku & Snowflake's help — can spot a real job posting from a fake one!\n\n\u003E \n\u003E \n\u003E  **About the data:** \u003Cbr\u003E The data for this quickstart comes from a Kaggle dataset of ~18000 job descriptions, out of which about 800 are fake. These are fairly simple datasets, once you have completed the lab you could consider enriching the project with additional data.\n\n### Prerequisites\n\n- Use of the Snowflake free 30-day trial environment\n- Basic knowledge of SQL, and database concepts and objects\n\n\n\n### What You'll Learn\n\nThe exercises in this lab will walk you through the steps to:  \n\n- Create databases, tables, views, and warehouses in Snowflake\n- Use Snowflake’s “Partner Connect” to seamlessly create a Dataiku DSS Cloud trial\n- Create a Data Science project in Dataiku and perform analysis on data via Dataiku within Snowflake\n- Use both visual and code tools\n- Create, run, and evaluate simple Machine Learning models in Dataiku\n- How at each step of the data science process you can utilise Dataiku and Snowflake in tandem to accelerate your team\n\n\n### What We’re Going To Build\n\nWe will build a project that uses input datasets from Snowflake. We’ll build a data science pipeline by applying data transformations, building a machine learning model, and deploying it to Dataiku's Flow. We will then see how you can score the model against fresh data from Snowflake and automate.\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Prepare Your Lab Environment\n\n\n- If you haven’t already, register for a [Snowflake free 30-day trial](https://trial.snowflake.com/) The rest of the sections in this lab assume you are using a new Snowflake account created by registering for a trial.\n\n\u003E \n\u003E \n\u003E  **Note**: Please ensure that you use the **same email address** for both your Snowflake and Dataiku sign up\n\n\n- **Region**  - Although not a requirement we'd suggest you select the region that is physically closest to you for this lab\n\n- **Cloud Provider**  -  Although not a requirement we'd suggest you select ```AWS``` for this lab\n\n- **Snowflake edition**  -  We suggest you select select the ```Enterprise edition``` so you can leverage some advanced capabilities that are not available in the Standard Edition.\n\n\n\nAfter activation, you will create a ```username```and ```password```. Write down these credentials. **Bookmark this URL for easy, future access**.\n\n\n\u003E \n\u003E \n\u003E  **About the screen captures, sample code, and environment:** \u003Cbr\u003E Screen captures in this lab depict examples and results that may slightly vary from what you may see when you complete the exercises.\n\n\u003C!-- ------------------------ --\u003E\n\n## The Snowflake User Interface\n\n\n### Logging Into the Snowflake User Interface (UI)\n\nOpen a browser window and enter the URL of your Snowflake 30-day trial environment. You should see the login screen below. Enter your unique credentials to log in.\n\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i1_login.png)\n\n### Close any Welcome Boxes and Tutorials\n\nYou may see “welcome” and “helper” boxes in the UI when you log in for the first time. Close them by clicking on `Skip for now` in the bottom right corner in the screenshot below.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i2_welcome.png)      \n\n### Navigating the Snowflke UI\n\nFirst let’s get you acquainted with Snowflake! This section covers the basic components of the user interface to help you orient yourself. We will move left to right in the top of the UI.\n\nThe main menu on the left allows you to switch between the different areas of Snowflake:\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i3_menu.png)\n\nThe **Databases** tab shows information about the databases you have created or have privileges to access. You can create, clone, drop, or transfer ownership of databases as well as load data (limited) in the UI. Notice several databases already exist in your environment. However, we will not be using these in this lab.   \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i4_databases.png)\n\nThe **Worksheets** tab provides an interface for submitting code queries, performing DDL and DML operations and viewing results as your queries/operations complete. \n\nIn the left pane is the database objects browser which enables users to explore all databases, schemas, tables, and views accessible by the role selected for a worksheet. The bottom pane will show results of queries and operations. \n\nIf this is the first time you’ve used Snowsight, you might be prompted to enable it.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/dataiku41.png)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i5_worksheets.png)\n\nAs you can see, there have already some worksheets been prepared for you to work with the demo data in the databases that we saw before. However, we are not going to use these existing worksheets now.\n\nInstead, we are going to create a new one. For that, please click on the blue `+` Button in the top right corner.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i6_new_worksheet.png)\n \nSelect `SQL Worksheet` from the menu and a new worksheet will be created and shown.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i7_new_worksheet.png)\n\n\nRename the newly created worksheet to **Job Postings** by clicking on the worksheet name and typing `Job Postings` and pressing ‘Enter’\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i7_worksheet_rename.png)\n\n\u003E \n\u003E \n\u003E  **Worksheets vs the UI** \u003Cbr\u003E Much of the configurations in this lab will be executed via this pre-written SQL in the Worksheet in order to save time. These configurations could also be done via the UI in a less technical manner but would take more time.\n\nThe **History** tab allows you to view the details of all queries executed in the last 14 days in the Snowflake account (click on a Query ID to drill into the query for more detail).\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i8_query_history.png)\n\n\nIf you click on the bottom left of the UI where your username appears, you will see that you can change your password, roles, or preferences. Snowflake has several system defined roles. You are currently in the default role of SYSADMIN. We will change this in the next part of the lab.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i9_profile_accountadmin.png)\n\n\n\u003E \n\u003E \n\u003E  **SYSADMIN** \u003Cbr\u003E For most of this lab you will remain in the SYSADMIN (aka System Administrator) role which has privileges to create warehouses and databases and other objects in an account. In a real-world environment, you would use different roles for the tasks in this lab, and assign the roles to your users. More on access control in Snowflake is in towards the end of this lab and also in our [documentation](https://docs.snowflake.com/user-guide/security-access-control-overview)\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Prepare Dataiku Trial Account Via Snowflake Partner Connect\n\n\n### Create Dataiku trial via Partner Connect\n\nAt the top right of the page, confirm that your current role is `ACCOUNTADMIN`, by clicking on your profile on the top right.\n\n1. Click on `Data Products` on the left-hand menu\n2. Click on `Partner Connect`\n3. Search for Dataiku\n4. Click on the `Dataiku` tile \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_1.png)\n\n\u003E \n\u003E Depending on which screen you are on you may not see the full menu as above but hovering over \n\u003E the Data Products (Cloud) icon will show the options\n\nThis will automatically create the connection parameters required for Dataiku to connect to Snowflake. Snowflake will create a dedicated database, warehouse, system user, system password and system role, with the intention of those being used by the Dataiku account.\n\nFor this lab we’d like to use the **PC_DATAIKU_USER** to connect from Dataiku to Snowflake, and use the **PC_DATAIKU_WH** when performing activities within Dataiku that are pushed down into Snowflake.\n\nThis is to show that a Data Science team working on Dataiku and by extension on Snowflake can work completely independently from the Data Engineering team that works on loading data into Snowflake using different roles and warehouses.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_9_partner_connect_connect.png)\n\nNote that the user password (which is autogenerated by Snowflake and never displayed), along with all of the other Snowflake connection parameters, are passed to the Dataiku server so that they will automatically be used for the Dataiku connection for this lab. **DO NOT CHANGE THESE**.\n\n1. Click `Connect`\n2. You will get a pop-ip which tells you your partner account has been created. Click on `Activate`\n\n\n\u003E \n\u003E \n\u003E  **Informational Note:** \u003Cbr\u003E If you are using a different Snowflake account than the one created \n\u003E at the start, you may get a screen asking for your email details. Click on ‘Go to Preferences’ and \n\u003E populate with your email details\n\n\n\n\nThis will launch a new page that will redirect you to a launch page from Dataiku.\n\nHere, you will have two options:\n\n1. Login with an existing Dataiku username\n2. Sign up for a new Dataiku account\n\nWe assume that you’re new to Dataiku, so ensure the “Sign Up” box is selected, and sign up with either GitHub, Google or your email address and your new password. Click sign up.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_2.png)\n\nWhen using your email address, ensure your password fits the following criteria:\n\n1. At least 8 characters in length\n2. Should contain:\n   Lower case letters (a-z)\n   Upper case letters (A-Z)\n   Numbers (i.e. 0-9)\n\nYou should have received an email from Dataiku to the email you have signed up with. Activate your Dataiku account via the email sent.\n\n\n\n### Review Dataiku Setup\n\nUpon clicking on the activation link, please briefly review the Terms of Service of Dataiku Cloud. In order to do so, please scroll down to the bottom of the page. Click on `I AGREE` and then click on `NEXT`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_3.png)\n\n\nComplete your sign up some information about yourself and then click on `Start`.\n\n\nYou will be redirected to the Dataiku Cloud Launchpad site. Click `GOT IT!` to continue.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_4.png)\n\nThis is the Cloud administration console where you can perform tasks such as inviting other users to collaborate, add plugin extensions, install industry solutions to accelerate projects as well as access community and academy resources to help your learning journey. \n\n\u003E \n\u003E**NOTE:** It may take several minutes for your instance to Dataiku to start up the first time,\n\u003E during this time you will not be able to add the extension as described below.\n\u003E You can always come back to this task later if time doesn't allow now\n\nIt's beyond the scope of this course to cover these but for this lab we would like to enable a few of the AI Assistants so lets do that now.\n\n1. Click on `Extensions` on the left menu\n2. Select `+ ADD AN EXTENSION` \n3. Find `AI Services` and click on it\n\nIn the AI Services Extension screen perform the following tasks:\n\n1. Agree to the terms and services\n2. Select `Enable AI Prepare`\n3. Select `Enable AI Explain`\n4. Click `ADD` \n5. Click on `Go Back To Space`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_PC_5a.png)\n\nYou’ve now successfully set up your Dataiku trial account via Snowflake’s Partner Connect. We are now ready to continue with the lab. For this, move back to your Snowflake browser.\n\n\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Preparing And Exploring The Data In Snowflake \n\n\n\n\n### Analysing the data using Snowsight\n\nNow that we’ve done some preparation work, let’s do some primarily data analysis on our data. For this we will use Snowsight, the SQL Worksheets replacement, which is designed to support data analyst activities.\n\nSnowflake recently released the next generation of it’s analytics UI — **Snowsight**. On top of a redesigned interface, there are many improvements for analysts, data engineers, and business users. With Snowsight, it is easier and faster to write queries and get results and collaboration with others through sharing makes it easier to explore and visualize data across your organization. Snowsight includes many features and enhancements, including: \n\n- **Fast query writing:** Includes smart autocomplete for query syntax keywords or listing values that match table/column names, data filters and quick access to Snowflake documentation for specific functions.\n- **Interactive query results:** View summary statistics about the data that has been returned by their query, using histograms of the distribution to identify outliers and anomalies.\n- **Attractive data visualizations:** Quickly analyze data without requiring an external analytics/visualization tool, with automatic chart generation and drag-and-drop interface for creating dashboards.\n- **Sharing and collaboration:** Share queries, worksheets, visualizations and dashboards securely among teams.\n- **Schema browser:** Search instantly across databases and schemas accessible by the current session role for tables, views, and columns whose names contain a specified string. Pin tables for quick reference to see column names and data types.\n\nFor more information on using Snowsight, see the [documentation](https://docs.snowflake.com/en/user-guide/ui-snowsight.html).\n\nLet’s run some preliminary analysis on the two tables that we’ll focus on. For this, we will select **Worksheets** under **Projects** in the top left corner.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_i10_start.png)\n\n\n\n\n### Data Problem\nSometimes you go through the entire process of building a predictive model and the predictions are quite poor and you trace the issue back to data problems.  In other cases, such as this one, the data changes with time and the models go bad.  \n\n\n\n### Preparing the Data for Further Data Analysis and Consumption\n\n#### Step 1 - Create Schema and Tables\nNow let's create the datastructuresa into which we are going to load the data. We will be using the database that was created when connecting to Dataiku - `PC_DATAIKU_DB` \n\nCopy the statements below into your worksheet and run them there.\n\n```sql\n   use warehouse PC_DATAIKU_WH;\n   use database PC_DATAIKU_DB; \n   create or replace schema RAW; \n   use schema RAW;\n\n   create or replace table EARNINGS_BY_EDUCATION (\n     EDUCATION_LEVEL varchar(100),\n     MEDIAN_WEEKLY_EARNINGS_USD decimal(10,2) \n   );\n\n   create or replace table JOB_POSTINGS (\n     JOB_ID int,\n     TITLE varchar(200),\n     LOCATION varchar(200),\n     DEPARTMENT varchar(200),\n     SALARY_RANGE varchar(20),\n     COMPANY_PROFILE varchar(20000),\n     DESCRIPTION varchar(20000),\n     REQUIREMENTS varchar(20000),\n     BENEFITS varchar(20000),\n     TELECOMMUNTING int,\n     HAS_COMPANY_LOGO int,\n     HAS_QUESTIONS int,\n     EMPLOYMENT_TYPE varchar(200),\n     REQUIRED_EXPERIENCE varchar(200),\n     REQUIRED_EDUCATION varchar(200),\n     INDUSTRY varchar(200),\n     FUNCTION varchar(200),\n     FRAUDULENT int\n   );\n```\n\n#### Step 2 - Load Data\nThe data we want to use is available as csv files. Hence we define a csv file format to make our lives easier\n\n```sql\ncreate or replace file format csvformat\ntype = csv\nfield_delimiter =','\nfield_optionally_enclosed_by = '\"', \nskip_header=1;\n```\n\nAs we have stored the data we want to load on an external S3 bucket, we need to create an external stage to load that data and also a stage for Dataiku to push Snowpark UDFs to.\n\n```sql\nCREATE OR REPLACE STAGE JOB_DATA\n  file_format = csvformat\n  url='s3://dataiku-snowflake-labs/data';\n\nCREATE or REPLACE STAGE DATAIKU_DEFAULT_STAGE;\n  \n ---- List the files in the stage \n\n list @JOB_DATA;\n```\n\nWith that all set, we are ready to load the data.\n\n```sql\ncopy into EARNINGS_BY_EDUCATION \nfrom @JOB_DATA/earnings_by_education.csv\non_error='continue';\n\ncopy into JOB_POSTINGS\nfrom @JOB_DATA/job_postings.csv\non_error='continue';\n```\n\nLet's a quick look at the data\n\n```sql\nselect * from RAW.EARNINGS_BY_EDUCATION limit 10;\n```\n\n![4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_4_query_education.png)\n\n```sql\nselect * from RAW.JOB_POSTINGS limit 10;\n```\n\n![5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_5_query_postings.png)\n\n#### Step 3 - Prepare Data for Analytics with Dataiku\nWith the data loaded into our ```raw``` stage, we want to prepare a table that joins the two sources into one, which we will then use in our workflow in Dataiku. \n\nLet's start by switching to the Public schema as the Dataiku connection created from Partner Connect has permissions on that.\n\n```sql\nuse schema PUBLIC;\n```\n\nAnd now on to the new table\n```sql\ncreate or replace table JOBS_POSTINGS_JOINED as\nselect \n    j.JOB_ID as JOB_ID,\n    j.TITLE as TITLE,\n    j.LOCATION as LOCATION,\n    j.DEPARTMENT as DEPARTMENT,\n    j.SALARY_RANGE as SALARY_RANGE,\n    e.MEDIAN_WEEKLY_EARNINGS_USD as MEDIAN_WEEKLY_EARNINGS_USD,\n    j.COMPANY_PROFILE as COMPANY_PROFILE,\n    j.DESCRIPTION as DESCRIPTION,\n    j.REQUIREMENTS as REQUIREMENTS,\n    j.BENEFITS as BENEFITS,\n    j.TELECOMMUNTING as TELECOMMUTING,\n    j.HAS_COMPANY_LOGO as HAS_COMPANY_LOGO,\n    j.HAS_QUESTIONS as HAS_QUESTIONS,\n    j.EMPLOYMENT_TYPE as EMPLOYMENT_TYPE,\n    j.REQUIRED_EXPERIENCE as REQUIRED_EXPERIENCE,\n    j.REQUIRED_EDUCATION as REQUIRED_EDUCATION,\n    j.INDUSTRY as INDUSTRY,\n    j.FUNCTION as FUNCTION,\n    j.FRAUDULENT as FRAUDULENT\nfrom RAW.JOB_POSTINGS j left join RAW.EARNINGS_BY_EDUCATION e on j.REQUIRED_EDUCATION = e.EDUCATION_LEVEL;\n```\n\nYour data should now look like this\n```sql\nselect * from PUBLIC.JOB_POSTINGS_JOINED;\n```\n![6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_6_query_postings_enriched.png)\n\n\n#### Step 4 - Grant Dataiku Access to Data\nAs a last step before heading over to Dataiku, we need to make sure that it can read the data we just loaded and joined. (Note: You wouldn't typically grant ALL like this but we are in isolated trial accounts)\n```sql\ngrant ALL on all schemas in database PC_DATAIKU_DB to role PC_Dataiku_role;\ngrant ALL privileges on database PC_DATAIKU_DB to role PC_Dataiku_role;\ngrant ALL on all stages in database PC_DATAIKU_DB to role PC_Dataiku_role;\n```\n\n\u003E \n\u003E \n\u003E  **Snowflake Compute vs Other Warehouses** \u003Cbr\u003E Many of the warehouse/compute capabilities we just covered, like being able to create, scale up and out, and auto-suspend/resume warehouses are things that are simple in Snowflake and can be done in seconds. Yet for on-premise data warehouses these capabilities are very difficult (or impossible) to do as they require significant physical hardware, over-provisioning of hardware for workload spikes, significant configuration work, and more challenges. Even other cloud data warehouses cannot scale up and down like Snowflake without significantly more configuration work and time.\n\n\u003E \n\u003E \n\u003E  **Warning - Watch Your Spend!**\n\nDuring or after this lab you should *NOT* do the following without good reason or you may burn through your $400 of free credits more quickly than desired:\n\n- Disable auto-suspend. If auto-suspend is disabled, your warehouses will continue to run and consume credits even when not being utilized.\n- Use a warehouse size that is excessive given the workload. The larger the warehouse, the more credits are consumed.\n\nWe are going to use the virtual warehouse `PC_DATAIKU_WH` for our Dataiku work. However, we are first going to slightly increase the size of the warehouse to increase the compute power it contains.\n\nOn the top right corner of your worksheet, click on the warehouse name. In the dialog, click on the three lines on the top right to get to the details page of the warehouses. There, change the size of the `PC_DATAIKU_WH` data warehouse from X-Small to Medium. Then click the “Finish” button.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/sf_d_warehouse.png)\n\nAlternatively, you can also run the following command in the worksheet:\n```sql\nalter warehouse PC_DATAIKU_WH set warehouse_size=MEDIUM;\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Creating And Running A Dataiku Project\n\n\nFor this module, we will login into the Dataiku hosted trial account and create a Dataiku project.\n\nHere is the project we are going to build along with some annotations to help you understand some key concepts in Dataiku. \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Final_Flow2.png)\n\n\n* A **dataset** is represented by a blue square with a symbol that depicts the dataset type or connection. The initial datasets (also known as input datasets) are found on the left of the Flow. In this project, the input dataset will be the one we created in the first part of the lab.\n\n* A **recipe** in Dataiku DSS (represented by a circle icon with a symbol that depicts its function) can be either visual or code-based, and it contains the processing logic for transforming datasets.\n\n* **Machine learning processes** are represented by green icons.\n\n* The **Actions Menu** is shown on the right pane and is context sensitive.\n\n* Whatever screen you are currently in you can always return to the main **Flow** by clicking the **Flow** symbol from the top menu (also clicking the project name will take you back to the main Project page).\n\n\u003E \n\u003E You can refer back to this completed project screenshot if you want to check your progress through the lab. (Note though that if you choose to use the \n\u003E SnowparkML plugin your final flow will look a little different)\n\n\u003E \n\u003E**NOTE:** If you didn't setup AI Assistants from the Extensions menu in the earlier Partner Connect lab do it now. \n\n### Creating a Dataiku Project\n\nGo back to your Dataiku Cloud instance landing page. \n\n1. Ensure you are on the `Overview` page\n2. Click on `OPEN INSTANCE` to get started.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Proj_1.png)\n\nCongratulations you are now using the Dataiku platform! For the remainder of this lab we will be working from this environment which is called the `design node`, its the pre-production environment where teams collaborate to build data products.\n\nNow lets create our first project. There are lots of existing options and accelerators available to us but for this lab we will start with a blank project.\n\n1. Click on the `+ NEW Project` button on the right hand side\n2. Select `Blank Project`\n3. Give your project a name such as `Jobs Fraud`\n4. Click on `Create`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Proj_2.png)\n\n\nSuccess! You’ve now created a dataiku project.\n\n\nClick on `Got it!` to minimize the pop-up on `Navigation and help in DSS` and return to the project home screen.\n\nReview the Dataiku DSS page. There are a few things to note from the project landing page on an example project:\n\n- The project name, image associated with the project, collaborators, and optional tags:\n\n\n\n- The number and types of objects in the project.\n\n\n\n- A description of the project written in markdown, can link specific Dataiku objects (e.g., datasets, saved models, etc.) in the description:\n\n\n\n- Summary of project (history is saved in a git log) as well as a Chat function for better collaboration:\n\n\n\n\n\n### Import Datasets\n\nImport the dataset from Snowflake\n\nClick on `+IMPORT YOUR FIRST DATASET`\n\n\n\nUnder SQL, select `Snowflake`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/dataiku64.png)\n\n1.To load the table, select the connection that was just created for us from `Partner Connect`. In the Table section select `Get Tables List`. Dataiku will warn you that this may be long list but we can OK this.\n\n2. Search for and select the `JOBS_POSTINGS_JOINED` table we just created in Snowflake.\n \n3. Then click `TEST TABLE` to test the connection\n\n4. If successful set the `New dataset name` (top right) to `JOBS_POSTINGS_JOINED` and click on `CREATE`.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_CON1.png)\n\n\nReturn to the flow by clicking on the `flow` icon in the top left *(keyboard shortcut g+f)*\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Flow1.png)\n\n\n\nDouble click on the `JOBS_POSTINGS_JOINED` dataset\n\n\n\nThe `JOBS_POSTINGS_JOINED` table contains data on a location and day basis about the number and types of cases (Active, Confirmed, Deaths, Recovered) that day.\n\nDataiku reads a sample of 10000 rows by default. The sampling method can be changed under `Configure Sample` but for this lab we can leave it as the default:\n\n\n\nDataiku automatically detects data type and meaning of each column. The status bar shows how much of the data is valid (green), invalid (red), and missing (grey). You can view column Stats (data quality, distributions) by clicking `Quick Column Stats` button on the right:\n\n\n\nClick the `close` button when you are finished\n\n\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Cleaning The Data With The Prepare Recipe\n\n\nAfter exploring our data we are going to perform some transformation steps to clean the data and generate new features.\n\n\n\u003E \n\u003E There are two really important concepts happening in this lab:\n\u003E\n\u003E **Firstly** The data stays in Snowflake. We work on a configurable sample of the data in memory, our dataset is quite small but it might not be \n\u003E and by working on a sample in memory we avoid unnecessary movement of data out of Snowflake.\n\u003E\n\u003E **Secondly** When you run the transformations you build in this section you may notice beneath the `RUN` button Dataiku specified the engine as `In-database`.\n\u003E Dataiku will always try to use the most efficient engine for any job and in this case it sees we \n\u003E are working on Snowflake data and will therefore push down to the Snowflake Virtual Warehouse \n\u003E that was created when we set up through Partner Connect.\n\u003E \n\u003E The ability of Dataiku to minimise data movement and push the code to where the data lives gives great benefits in terms of performance, costs and governance. \n\n\nDataiku terms these transformation steps as `Recipes` and they may be visual (UI) or code based (a variety of editors, notebooks and IDE's are available). \n\nLets start with a visual recipe called the `Prepare` recipe. You can think of this recipe like a toolbox with lots of different tools for a variety of data transformation tasks. You build a series of transformation steps and check their effect on a sample of the data before pushing them to the full dataset.\n\n1. Select your dataset from the flow (remember you can use the `g+f` keyboard shortcut)\n2. After highlighting the dataset by clicking on it once go to the right hand actions menu select the `Prepare` recipe from the Visual Recipes list\n3. You can leave the defaults and click on `CREATE RECIPE`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prepare1.png)\n\n\n\n### Location Column\n\nLooking at our data we can see the location column has a lot of information contained within it that could make useful features for our model however in its current comma separated string format it is not that useful. Lets use the `Split` processor to pull out the location information into their own columns.\n\n1. Click on the `+ ADD A NEW STEP` button on the left\n2. You can use the search window to find the split processor\n3. Select the `Split Column` processor. \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_Split1.png)\n\nA new step is added to script on the left. We now need to populate the fields so Dataiku knows how we'd like to apply the split.\n\n1. For the column we want to enter `location` \n2. It's comma separated so the delimiter will be `,`\n3. We can leave the prefix as the default\n4. Select the `Truncate` option\n5. Since there are three comma separated location values change the columns to keep to `3`\n6. As you fill in the values you can see the effects live in the blue columns which is a great way of understanding the impact of the changes you are making and if it is the desired outcome.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_Split2.png)\n\n\u003E \n\u003E In addition to `g+f` one of the other most useful keyboard shortcuts is `c` when your are in the Explore tab. This allows you to search and scroll to a \n\u003E particular column. Very useful for wider datasets. \n\u003E Take a look at the [Documentation](https://doc.dataiku.com/dss/latest/accessibility/index.html) for more.\n\n\nSplitting the column was useful but lets make the column names a little more human readable. We can use the rename processor for this. Select the `Rename` processor just like you did for Split and then click on `+Add Renaming` and rename location_0 to country. Repeat for location_1 and location_2 changing them to state and city respectively. The step should look like this\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prep_rename.png)\n\n\u003E \n\u003E You could also achieve this by right clicking on the column name and selecting `rename`. \n\u003E When you right click on a column Dataiku makes suggestions on the most common transformations based\n\u003E on the type of data in the column.  \n\n### Text Columns\n\nNext we have a number of text columns. When building a machine learning model there are a number of techniques we can use to work with text data, we are going to simplify the text and use the Normalise feature which transforms to lowercase, removes punctuation and accents and performs Unicode NFD normalization (Café -\u003E cafe).\n\nWe could search for the processor we want and configure it like before but since we are new to Dataiku lets use the AI Prepare assistant to help us out this time. We can describe the steps we want and allow the AI Assistant to look through the 100+ processors and configure them to our requirements.\n\n1. Click on `AI PREPARE` button on the left side of the screen\n2. In the text box paste in the following prompt and then click on `GENERATE`\n\n```\nnormalize text for the columns COMPANY_PROFILE, DESCRIPTION, REQUIREMENTS, BENEFITS. \ndont create a new column, update in place\n```\nThe AI Assitant generates the 4 steps for us and documents them to make the results are easy to review for everyone using the data preparation job\n\nNow we have normalized the text in those columns we might consider creating a new feature based on the length. Our theory might be that scammers will focus on the salary and buzzwords to get people to apply and are less likely to populate the job description and company background. \n\nAgain if we know the processor we want we can just search and use it directly. In our case as we're new to Dataiku let's use the AI Prepare assistant to help us out. \n\n1. Click on `AI PREPARE` button on the left side of the screen\n2. In the text box paste in the following prompt and then click on `GENERATE`\n\n```\ncalculate the length of the columns COMPANY_PROFILE, DESCRIPTION, REQUIREMENTS, BENEFITS.\nwrite them to new columns with the prefix LENGTH_\n```\n\nIf your script now matches the below screenshot go ahead and click on the green `RUN` button at the bottom of the script.\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Prepare_finish.png)\n\n\n\u003E \n\u003E Using AI Assistants in this way can be a very powerful tool but it is important to review the generated steps to ensure that it achieves \n\u003E your aims accurately.\n\n\n\u003C!-- ------------------------ --\u003E\n## Feature Engineering With Snowpark\n\n\nIn addition to a wide number of visual tools to enable to the low/no coder Dataiku also provides rich and familiar toolsets and language support for coders. \n\nIn this section we will put ourselves in the shoes of a data scientist that is collaborating on the project. Whilst they can get value from tools like the Prepare recipe they may be looking for full code experience so in this section we will use the built-in support in Dataiku for notebooks and IDE's\n\nLets use a Jupyter notebook to create a Snowpark for Python function to extract the minimum salary range\n\nWhen using Dataiku's SaaS option from Partner Connect the setup is done for us automatically and we checked that in our earlier lab where we set up the AI Services. If for any reason you skipped that step earlier then return to your browser tab with `Dataiku Launchpad` open (if you have shut this just go to [Launchpad](https://launchpad-dku.app.dataiku.io/) and check that `Snowpark` is enabled under the `Extensions` \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark1.png)\n\n### Snowpark code\n\n\u003E \n\u003E **Integrations:** Much like in our last chapter here we are using Dataiku's deep integrations with Snowflake to work on data in the most efficient way. Our data scientist can \n\u003E use the tools they are most familiar with in Dataiku whilst also collaborating on the project with non-coding colleagues and even packaging custom code-based functions in a visual interface to expose complex tasks to less technical users. The data is loaded into a \n\u003E Snowpark Python DataFrame and when we execute our code we push the computation to Snowpark.\n\nLets create our Python code recipe:\n\n1. From the flow select the output dataset from our prepare recipe and then from the actions menu on the right select `Python` from the code recipes section.\n2. In the `Outputs` section click `+ ADD`\n3. Let's name our new output dataset `Jobs_Python`\n4. Click `Create Dataset`\n5. Click `CREATE RECIPE`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark1a.png)\n\nWe need to set a code environment that has the correct packages in. Fortunately that has been created for us, we just need to select it for this recipe.\n\n1. Click on the `Advanced` tab at the top of the screen\n2. Under the Python Env. section change the `Selection behaviour` to `Select an environment`\n3. In the `Environment` drop down select the `snowpark` code environment.\n4. Click `Save`\n5. Select `Code` tab to return to the main editor\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark_1b.png)\n\n\n\u003E \n\u003E **A Note on Code Environments:**  Dataiku uses the concept of code environments to address the problem of managing dependencies and \n\u003E versions when writing code in R and Python. Code environments provide a number of benefits such as **Isolation and Reproducibility** of \n\u003E results. When using Snowpark for Python from Dataiku you will use a code environment that includes the Snowpark library as well as other\n\u003E packages you wish to use. In our lab, to make things easy, we are using a default Snowpark code environment which just contains just the\n\u003E minimum required libraries but once you have completed the lab and wish to explore further you can create your own code environments.\n\n\nIn addition to selecting an appropriate code environment there are just a couple of extra lines of code to add to your DSS recipe to start using Snowpark for Python \n\n\n Helpfully we can use one of the many code samples available to us.\n\n1. Delete the automatically generated python starter code\n2. Click on the `{CODE SAMPLES}` button \n3. Search for Snowpark \n4. Select the `Read and write datasets with Snowpark` option\n3. Click `+ INSERT`\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark2a.png)\n\n\nYou now have some starter Snowpark code with the correct input and output dataset names. \n\nWe could carry on using the default code editor if we wish but we also have the option to use notebooks or IDE's so lets go ahead and use the in-built Jupyter notebook for the next part.\n\n1. Click on `EDIT IN NOTEBOOK` option in the top-right. We are going to use the Dataiku package and some Snowpark functions so lets add that now and feel free to separate into cells if you wish. Add the following two lines at the start of your code:\n\n```\n#add these two lines at the start of your code\nimport dataiku\nfrom snowflake.snowpark.functions import *\n\n```\n\nNow lets take a simple example of feature engineering in code. `Delete` the section that reads:\n\n```\n# TODO: Replace this part by your actual code that computes the output, as a Snowpark dataframe\n# For this sample code, simply copy input to output\noutput_dataset_df = input_dataset_df\n```\n\nLets replace that deleted section with our Snowpark for Python code to generate a new feature called `min_salary` \n\n```\n#strip minimum salary from the given range\noutput_dataset_df = input_dataset_df.withColumn('\"MIN_SALARY\"', split(col('\"SALARY_RANGE\"'), lit('-'))[0])\n```\n\n\u003E \n\u003E Of course this is a very simple piece of feature engineering and our data scientist could go much further but it demonstrates how our code first users can easily work alongside their colleagues\n\u003E\n\nYour code should now look similar to this (don't worry if you haven't separated your code into cells)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowpark3.png)\n\n1. Run your cell(s) to make sure your code is correct\n2. Click the `SAVE BACK TO RECIPE` button near the top of the screen\n3. From the default code editor click `RUN`\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Split The Dataset\n\n\n\u003E \n\u003E For the remainder of this lab we will be using Dataiku's Visual ML interface to design, train & test our model. This is the best option for most circumstances, however if you are specifically interested in trying out Snowflakes SnowparkML library then you could, of course, write that code from Dataiku as we just saw with the Python recipe but, even better, Dataiku provides a UI via a plugin so non-coders can use it. If you wish to develop your model using that plugin then jump to the optional chapter on the SnowparkML plugin near the end of this guide\n\u003E\n\nOne advantage of an end-to-end platform like Dataiku is that data preparation can be done in the same tool as machine learning. For example, before building a model, you may wish to create a holdout set. Let’s do this with a visual recipe.\n\n### Steps\n\n1. From the Flow, click the `Jobs_Python` dataset once to select it.\n\n2. Open the Actions tab on the right.\n\n3. Select the Split recipe from the menu of visual recipes.\n\n4. Click `+ Add`; name the output `train`; and click Create Dataset.\n\n5. Click `+ Add` again; name the second output `test`; and click Create Dataset.\n\n6. Once you have defined both output datasets, click `Create Recipe`.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Split1a.png)\n\n### Define a split method\n\n On the Splitting step of the recipe, choose `Dispatch percentiles of sorted data` as the splitting method.\n\n1. Set to sort according to `JOB_ID`\n2. Set the ratio of 80 % to the `train` dataset, and the remaining 20% to the `test` dataset.\n3. Click the green Run at the bottom left to build these two output datasets.\n\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Split2.png)\n\nWhen the job finishes, navigate back to the Flow (g + f) to see your progress.\n\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Train A Model\n\n\nThe first step is to define the basic parameters of the machine learning task at hand.\n\n 1. Select the train dataset.\n\n 2. In the Actions tab, click on the Lab button. Alternatively, navigate to the Lab tab of the right side panel (shown below).\n\n 3. Among the menu of visual ML tasks, choose AutoML Prediction.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Train1.png)\n\nNow you just need to choose the target variable and which kind of models you want to build.\n\n1. Choose `FRAUDULENT` as the target variable on which to create the prediction model.\n\n2. Click Create, keeping the default setting of Quick Prototypes.\n\n![64](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_2.png)\n\n### Train models with the default design\n\nBased on the characteristics of the input training data, Dataiku has automatically prepared the design of the model. But no models have been trained yet!\n\n1. Before adjusting the design, click Train to start a model training session.\n\n2. Click Train again to confirm.\n\n![64](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_3.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Inspect The Results\n\n\nOnce your models have finished training, let’s see how Dataiku did.\n\n1. While in the Result tab, click on the Random forest model in Session 1 on the left hand side of the screen to open a detailed model report.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect1.png)\n\n### Check Model Explainability - Feature Importance\n\nOne important aspect of a model is the ability to understand its predictions. The Explainability section of the report includes many tools for doing so.\n\n1. In the Explainability section on the left, click to open the `Feature importance` panel to see an estimate of the influence of a feature on the predictions.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect2.png)\n\n### Check Model Explainability - Confusion Matrix\n\nA useful tool to evaluate and compare classification models is the confusion matrix. This compares the actual values of the target variable to our models predictions broken down into where the model got it right (true positives & true negatives) and where it got it wrong (false positives & false negatives).\n\n1. In the Performance section on the left, click to open the `Confusion Matrix` panel\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect3.png)\n\n\n\n\n### Check Model Explainability - What If?\n\nWhat if analyses can be a useful exercise to help both data scientists and business analysts get a sense for what a model will predict, given different input values. You can use the drop-down menus and sliders to adjust the values, type in your own, or even choose to ignore features to simulate a situation with missing values. On the right, you can review the new prediction based on your inputs.\n\n1. Click on the `What If?` to open the panel.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Inspect4.png)\n\n\n\n\n\n### Check Model Information\n\nAlongside the results, you’ll also want to be sure how exactly the model was trained.\n\n1. In the Model Information section, click to open the Features panel to check which features were included in the model, which were rejected (such as the text features), and how they were handled.\n\n2. When finished, at the top of the model report, click on Models to return to the Result home.\n\n![65](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Model_info.png)\n\n\n\u003E \n\u003E There are many more features to better understand your model. Feel free to explore them as time permits\n\n\u003C!-- ------------------------ --\u003E\n## Iterate On The Model Training Design (optional)\n\n\n\u003E \n\u003E This chapter is optional in the lab for timing reasons but would be a standard part of real world model development. \n\u003E Feel free to cover it now if you have time or return to it later to improve your model\n\nThus far, Dataiku has produced quick prototypes. From these baseline models, you can work on iteratively adjusting the design, training new sessions of models, and evaluating the results.\n\n1. Switch to the `Design` tab at the top center of the screen.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate1.png)\n\n### Tour the Design tab\n\nFrom the Design tab, you have full control over the design of a model training session. Take a quick tour of the available options. Some examples include:\n\n1. In the `Train / Test Set` panel, you could apply a k-fold cross validation strategy.\n\n2. In the `Feature reduction` panel, you could apply a reduction method like Principal Component Analysis.\n\n3. In the `Algorithms` panel, you could select different machine learning algorithms or import custom Python models.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate2.png)\n\n### Reduce the number of features\n\nInstead of adding complexity, let’s simplify the model by including only the most important features. Having fewer features could hurt the model’s predictive performance, but it may bring other benefits, such as greater interpretability, faster training times, and reduced maintenance costs.\n\n1. In the `Design` tab, navigate to the `Features handling` panel on the left.\n\n2. Click the box at the top left of the feature list to select all.\n\n3. For the role, click `Reject` to de-select all features.\n\n4. Turn on the three most influential features according to the Feature importance chart seen earlier: `COUNTRY, HAS_COMPANY_LOGO, LENGTH_COMPANY_PROFILE`.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate3.png)\n\n\u003E \n\u003E Your top three features may be slightly different. Feel free to choose these three or the three most important from your own results.\n\n### Train a second session \n\nOnce you have just the top three features in the model design, you can kick off another training session.\n\n1. Click the blue `Train` button near the top right to start the next session.\n2. Click `Train` once more to confirm.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Iterate4.png)\n\n\u003E \n\u003E In reality our results from both training runs are suspiciously high and would merit further investigation. Indeed if you click on the diagnostics that Dataiku helpfully runs for each training session you can see a warning for an imbalanced dataset. If you switch the metric to `F1` (which is a better metric for imbalanced datasets) you will see a significant drop in score. There are many ways Dataiku can help, for example with the `class rebalancing` sampling method. It is beyond the scope of this course but read up in our \n\u003E documentation or blogs or take one of the more advanced Dataiku Academy ML courses to understand how Dataiku ML Diagnostics can help you identify and troubleshoot potential issues and suggest possible improvements as you build your model. \n\n\u003C!-- ------------------------ --\u003E\n\n## Apply A Model To Generate Predictions On New Data\n\n\nUp until now, the models you’ve trained are present only in the Lab, a space for experimental prototyping and analysis. You can’t actually use any of these models until you have added them to the Flow, where your actual project pipeline of datasets and recipes lives. Let’s do that now!\n\n### Choose a model to deploy\n\nMany factors could impact the choice of which model to deploy. For many use cases, the model’s performance is not the only deciding factor.\n\nCompared to the larger model, the simple model with three features cost about 4 hundredths of a point in performance. For some use cases, this may be a huge amount, but in others it may be a bargain for a model that is more interpretable, cheaper to train, and easier to maintain. Since performance is not too important in this tutorial, let’s choose the simpler option.\n\n1. From the Result tab, click the Random forest (s2) to open the model report of the simpler random forest model from Session 2.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score1.png)\n\nNow you just need to deploy this model from the Lab to the Flow.\n\n1. Click `Deploy` near the top right.\n2. Click `Create` to confirm.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score2.png)\n\n### Score Data\n\nYou now have two green objects in the Flow that you can use to generate predictions on new data: a training recipe and a saved model object.\n\n1. From the Flow single click on the diamond-shaped saved model to select it\n2. From the Actions menu select the `Score` recipe\n3. For the `Input Dataset` select the `test` dataset\n4. Click `CREATE RECIPE`\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score3a.png)\n\n1. From the Score recipe you can leave the defaults but make sure that Snowflake Java UDF is selected as the engine. If it isn't click on the gear cog and select it. When you are done click `RUN` \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score4.png)\n\n\u003E \n\u003E You may notice the `..Java UDF` part of that engine. This is one of a number of places that Dataiku embeds Snowpark Java UDFs into the product for the \n\u003E best integration and performance. From your perspective as a user Dataiku will take care of the details and it simply means the task at hand runs faster\n\n\n\n### Inspect the scored data\n\nCompare the schemas of the test and test_scored datasets.\n\n1. When the job finishes, click Explore dataset `test_scored`.\n2. Scroll to the right, and note the addition of three new columns: `proba_0, proba_1, and prediction`.\n3. Navigate back to the Flow to see the scored dataset in the pipeline.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Score6.png)\n\n\u003E \n\u003E How well was the model able to identify the fake job postings in the test dataset? That is a task for the Evaluate recipe, which you will encounter in other learning resources.\n\n\u003C!-- ------------------------ --\u003E\n\n## Document The Flow (optional)\n\n\n\u003E \n\u003E This chapter is optional in the lab for timing reasons but documenting your project along with other capabilities in Dataiku like automatic generation of model documentation is important in MLOps) \n\nDataiku can generate explanations of project Flows. The feature leverages a Large Language Model (LLM) to do this.\n\n- On the Flow screen open the Flow Actions menu\n- Select Explain Flow\n\nIt is possible to adjust the generated explanations for language, purpose and length. Apply the following and then set the generated text as the project description.\n\n- Language: English\n- Purpose: Business\n- Length: Medium\n\n![64](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Explainflow_1.png)\n\n\u003C!-- ------------------------ --\u003E\n\n\n\n## Using Snowpark ML Plugin (optional)\n\nSnowflake recently released a collection of python APIs enabling efficient ML model development directly in Snowflake. You can, of course, use this library directly from Dataiku in a code recipe but we also provide a free to use plugin to provide a UI.\n\nThere are a few steps you need to take to install the plugin and prepare the data.\n\n### Install the plugin\n\n1. Return the Dataiku Cloud launchpad (https://launchpad-dku.app.dataiku.io)\n2. In the `Plugins` section select `+ ADD A PLUGIN`\n3. Search for and install the Visual SnowparkML plugin\n\n### Data pre-processing\n\nWhen using the plugin there are a few additional pre-processing steps necessary that we don't need to do when using Dataiku's standard Visual ML interface. Firstly we would need to make sure that all the column names are in uppercase but fortunately in our dataset that is already the case. Secondly we need to make sure that any columns of type `int` that have missing values are converted to `doubles`\n\n1. Click once on the `Jobs_Python` dataset in the flow to select it and then choose the `Prepare` recipe from the Actions menu, just like we did earlier in the lab\n2. There are a number of columns of type `int` with missing values. Change these to doubles by clicking on the datatype under the column name and selecting it.\n3. Click `RUN`\n\n### SnowparkML Plugin\n\nNow we have performed our preprocessing select the output dataset and then the plugin from the `Actions` menu (Note: You may need to scroll down to find the plugins, they are below the code and LLM recipes)\n\nThere are a number of output fields to fill:\n\n1. Set an output dataset name for the `Train Dataset Output`\n2. Set an output dataset name for the `Test Dataset Output`\n3. Set a name for `Model Folder` where the MLflow experiment tracking data and trained models will be stored\n4. Optionally you can set a folder for the final best model but we can leave this blank\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowml1.png)\n\nNow we can set the details of our training run. \n\n1. Give the final model a name\n2. Set the target column to `Fraudulent`\n3. This is a `Two-class classification` problem\n4. The ratio can be set to `0.8` for the standard 80/20 split and a random seed can also be set.\n\nYou can now set your Metrics, Features, Algos and more for your training session. Just click `RUN` at the bottom left when you are happy with your setup\n\n ![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-dataiku-and-snowflake-guide-to-data-science/DKU_Snowml2.png)\n\nCongratulations. You are using SnowparkML from a UI! You can explore your model from the `MLflow` green diamond in the `Flow` looking at explainability and performance measures, model comparisons and much more.\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusions And Resources\n\n\nCongratulations on completing this introductory lab exercise! Congratulations! You've mastered the Snowflake basics and you’ve taken your first steps toward data cleansing, feature engineering and training machine learning models with Dataiku.\n\nYou have seen how Dataiku's deep integrations with Snowflake can allow teams with different skill sets get the most out of their data at every stage of the machine learning lifecycle.\n\nWe encourage you to continue with your free trial and continue to refine your models and by using some of the more advanced capabilities not covered in this lab.\n\n### What You Learned:\n\n- How to create stages, databases, tables, views, and virtual warehouses.\n- How to load structured and semi-structured data.\n- How to perform analytical queries on data in Snowflake, including joins between tables.\n- How to create a Dataiku trial account through Partner Connect\n- How to use both Visual and Code Recipes to explore and transform data\n- How to train, explore and understand a machine learning model\n\n### Related Resources\n\n- Join the [Snowflake Community](https://community.snowflake.com/s/)\n- Join the [Dataiku Community](https://community.dataiku.com/)\n- Sign up for [Snowflake University](http://https://community.snowflake.com/s/snowflake-university)\n- Join the [Dataiku Academy](https://academy.dataiku.com/)\n\n\n\n\n","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-7cdec8adc9","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-98dd652297","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-d6eb9d8cbe","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-0e8e52f853","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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