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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-116f09cb6f","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Partner Solution","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/partner-solution","tagIcon":""},"quickstartHeroTitle":{"lines":["AutoML with Snowflake and H2O Driverless 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machine learning platform leveraging the concept of automated machine learning. Supervised machine learning is a method that takes historic data where the response or \u003Cstrong\u003Etarget\u003C/strong\u003E is known and build relationships between the input variables and the target variable. Driverless AI automates most of difficult supervised machine learning workflow such as feature engineering, model validation, model tuning, model selection, and model deployment. Modeling pipelines, which are produced from H2O Driverless AI, can exported as standalone scoring artifacts to power your AI/ML use case.\u003C/p\u003E\n","\u003Cp\u003EThis tutorial presents a quick introduction to the Driverless AI platform via Snowflake Partner Connect.\u003C/p\u003E\n","\u003Cp\u003EWe will use a dataset from LendingClub.com to build a classification model to help us predict the likelihood a LendingClub.com borrower will default on their loan. LendingClub.com is an established online loan marketplace that funds personal loans, commercial loans, funding of medical procedures, and other financing needs. The data consist of 25 columns and approximately 39,000 rows, with each row corresponding to a customer. Here is preview of the data:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/00_intro_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/00_intro_02.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENote that the dataset consist of numerical columns (\u003Ccode\u003Eloan_amount\u003C/code\u003E, \u003Ccode\u003Einstallment\u003C/code\u003E, \u003Ccode\u003Eemp_length\u003C/code\u003E, \u003Ccode\u003Edti\u003C/code\u003E, etc.), categorical columns (\u003Ccode\u003Eterm\u003C/code\u003E, \u003Ccode\u003Ehome_ownership\u003C/code\u003E, \u003Ccode\u003Everification_status\u003C/code\u003E, \u003Ccode\u003Epurpose\u003C/code\u003E, etc.), and a text column (\u003Ccode\u003Edesc\u003C/code\u003E). Our target variable is \u003Ccode\u003Ebad_loan\u003C/code\u003E which is a Boolean with values \u003Ccode\u003ETrue\u003C/code\u003E and \u003Ccode\u003EFalse\u003C/code\u003E, thus this will be a binary classification problem.\u003C/p\u003E\n","\u003Cp\u003EWe will use Snowflake and Driverless AI to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EImport\u003C/strong\u003E the data from Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExplore\u003C/strong\u003E the data using summary descriptive statistics and automated visualizations (AutoViz)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuild\u003C/strong\u003E a predictive model using an evolutionary algorithm for automatic feature engineering and model optimization\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMeasure\u003C/strong\u003E the model through diagnostics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnderstand\u003C/strong\u003E the model through MLI (machine learning interpretability)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeploy\u003C/strong\u003E the model into production in a Snowflake system\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E Account deployed in AWS (if you are using an enterprise account through your organization, it is unlikely that you will have the privileges to use the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role, which is required for this lab)\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://www.h2o.ai/try-driverless-ai/\"\u003EH2O\u003C/a\u003E trial license key\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\"\u003ESnowSQL\u003C/a\u003E installed (Snowflake's CLI tool)\u003C/li\u003E\u003Cli\u003EPast experience running and executing queries in Snowflake\u003C/li\u003E\u003Cli\u003EA basic understanding of data science and machine learning concepts\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake's "Partner Connect" to create a Driverless AI instance\u003C/li\u003E\u003Cli\u003EHow to use Driverless AI to build a supervised learning classification model\u003C/li\u003E\u003Cli\u003EHow to deploy the finished model pipeline as a Snowflake Java UDF\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E\n","\u003Cp\u003EThe first thing you will need to do is download the following .sql file that contains a series of SQL commands we will execute throughout this lab.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://snowflake-workshop-lab.s3.amazonaws.com/h2o/Snowflake_H2o_VHOL_guides.sql\"\u003EDownload .sql File\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EAt this point, log into your Snowflake account and have a clear screen to start working with. If you have just created a free trial account, feel free to minimize or close any hint boxes that are looking to help guide you. These will not be needed for this lab as most of the hints will be covered throughout the remainder of this exercise.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p5.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo ingest our script in the Snowflake UI, navigate to the ellipsis button on the top right hand side of a “New Worksheet” and load our script.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides "worksheets" as the spot for you to execute your code. This lab assumes you have already run a few queries in Snowflake before. Therefore, we are going to execute a series of commands quickly, so we get the data in tables and continue to the more interesting part of the lab of building and deploying models. The .sql file that you upload should look like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE PC_H2O_ROLE;\nUSE DATABASE PC_H2O_DB;\nUSE SCHEMA public;\nUSE WAREHOUSE PC_H2O_WH;\n\nCREATE OR REPLACE TABLE loans (\n    id INTEGER,\n    loan_amnt INTEGER,\n    term String(1024),\n    installment Real,\n    grade String(1024),\n    ...)\n\n...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote: before you execute the SQL statements, please proceed to the next section to connect to H2O and launch your Driverless AI instance.\u003C/strong\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELaunching Driverless AI\u003C/h2\u003E\n","\u003Cp\u003ESnowflake's Partner Connect feature allows you to seamlessly get started with partner tools and manages most of the connection details for you to get up and running as quickly as possible.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p20.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGo ahead and click on the "Partner Connect" application. This should take you to the following screen where you will see many of the Snowflake partners, and through a simple method of setting up an account and integration, allow you to quickly move data into a partner tool.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p21.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo be able to continue test out partner applications, in our case H2O, we need to promote ourselves to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role. This is an out of worksheet process, and therefore isn't a command for us to run. We need to do this one manually.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p22.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you have completed this step, go ahead and click on the H2O application. This will present you with a screen to connect to H2O. It will outline a number of Snowflake objects that will be auto-created. For the purposes of this lab, we have already created the snowflake objects that we will need, so you can press "Connect" .\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis creates a partner account which you can immediately \u003Ccode\u003EActivate\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_02.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou next need to accept the H2O Terms and Conditions for the Trial Agreement\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_03.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand wait while your H2O Driverless AI instance is configured and launched.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_04.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EDriverless AI Interface\u003C/h3\u003E\n","\u003Cp\u003EYour brand new Driverless AI instance looks like\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_intro_0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA summary of the information and views we will cover in this tutorial include:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EH2O.ai information: This displays the version (Driverless AI 1.9.0.2), the license owner and status, and the current user (H2OAI).\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EDATASETS\u003C/code\u003E: A view for importing, listing, and operating on datasets.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EAUTOVIZ\u003C/code\u003E: The Automatic Visualizations of data view.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EEXPERIMENTS\u003C/code\u003E: The view where we build and deploy predictive models.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EDIAGNOSTICS\u003C/code\u003E: Model diagnostics view.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EMLI\u003C/code\u003E: Machine learning interpretability view, information to help us understand our models.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ERESOURCES\u003C/code\u003E: A pull-down menu for accessing system information, clients, help, and other resources.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch2\u003ELoading dataset and creating a Snowflake table\u003C/h2\u003E\n","\u003Cp\u003ENow let's setup the database and warehouse in Snowflake, and create a table to use for the lab.\u003C/p\u003E\n","\u003Cp\u003EIn the Snowflake worksheet, you have previously loaded a \u003Ccode\u003E.sql\u003C/code\u003E script. The SQL commands in this script will import the Lendingclub dataset and create a table called \u003Ccode\u003Eloans\u003C/code\u003E. This table will be used with H2O Driverless AI to train and deploy a machine learning model.\u003C/p\u003E\n","\u003Cp\u003ETo execute the entire .sql code, which contains 9 different statements, all we need to do is click on the "All Queries" button next to blue "run" button at the top left of the worksheet and then press "run". You should see the "run" button has a "(9)", meaning it will execute all 9 commands in the uploaded file.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImport Data from Snowflake\u003C/h2\u003E\n","\u003Cp\u003EFrom the empty Datasets view, click the \u003Ccode\u003EAdd Dataset\u003C/code\u003E button and select the \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E connector:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_data_1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis launches the \u003Ccode\u003EMake Snowflake Query\u003C/code\u003E form.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_2.png\" alt=\"\"\u003E\nEnter into the form:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E \u003Ccode\u003EPC_H2O_DB\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E as \u003Ccode\u003EPC_H2O_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E as \u003Ccode\u003EPUBLIC\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EName\u003C/strong\u003E as \u003Ccode\u003Eloans.csv\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUsername\u003C/strong\u003E and \u003Cstrong\u003EPassword\u003C/strong\u003E with the credentials you used at signup\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFile Formatting Parameters\u003C/strong\u003E as \u003Ccode\u003EFIELD_OPTIONALLY_ENCLOSED_BY = '"'\u003C/code\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Enote: the quotation marks are \u003Cstrong\u003E\u003Cem\u003Esingle double single\u003C/em\u003E\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESQL Query\u003C/strong\u003E \u003Ccode\u003ESELECT * FROM LOANS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThen click the \u003Ccode\u003ECLICK TO MAKE QUERY\u003C/code\u003E button. This imports the data into the Driverless AI system.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_5.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe dataset is now available for next steps in Driverless AI\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_3.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDataset Details\u003C/h2\u003E\n","\u003Cp\u003ERight click the \u003Ccode\u003Eloans\u003C/code\u003E dataset to get details.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EDataset Details\u003C/code\u003E view is a quick way to inspect the dataset columns, see their storage type (integer, string, etc.), get summary statistics and distribution plots for each column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn more advanced usage, you can edit the data type interactively\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EScrolling to the right, inspect the \u003Ccode\u003Ebad_loans\u003C/code\u003E column, our target variable.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe target \u003Ccode\u003Ebad_loans\u003C/code\u003E is Boolean with 38,980 observations and has a mean value of 0.1592. This means that 15.92% of the customers (rows) in this dataset have a loan that was not paid off.\u003C/p\u003E\n","\u003Cp\u003EClicking the \u003Ccode\u003EDATASET ROWS\u003C/code\u003E button on the upper right yields a spreadsheet format.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is helpful in understanding the layout of the data. A quick inspection of your dataset using \u003Ccode\u003EDetails\u003C/code\u003E is a good practice that we always recommended.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVisualizing Datasets\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003EAutoviz\u003C/code\u003E in Driverless AI automatically creates a variety of informative interactive graphs that are designed for understanding the data to be used in building a predictive model. \u003Ccode\u003EAutoviz\u003C/code\u003E is unique in that it only shows the graphs that are applicable for your data based on the information in your data.\u003C/p\u003E\n","\u003Cp\u003ERight click the dataset name and select \u003Ccode\u003EVISUALIZE\u003C/code\u003E to launch AutoViz\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_00.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe available visualizations for the \u003Ccode\u003Eloans\u003C/code\u003E data are shown below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_02.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelecting the \u003Ccode\u003ESKEWED HISTOGRAMS\u003C/code\u003E section, for example, yields a series of histograms on only the columns that are sufficiently skewed. We show one below for the \u003Ccode\u003Ecredit_length\u003C/code\u003E column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_03.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClicking the left and right navigation arrows allows you to inspect additional variables, ordered by their skewness.\u003C/p\u003E\n","\u003Cp\u003EClose the \u003Ccode\u003ESKEWED HISTOGRAMS\u003C/code\u003E display and scroll down to see \u003Ccode\u003ERECOMMENDATIONS\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelecting \u003Ccode\u003ERECOMMENDATIONS\u003C/code\u003E produces\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_05.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe philosophy underlying automatic visualizations is to make it easy for the data scientist to quickly understand their data fields, but it does not make decisions for the data scientist.\u003C/p\u003E\n","\u003Cp\u003EThere are a number of additional useful graphs that can be navigated to fully understand your data prior to modeling.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESplit Data\u003C/h2\u003E\n","\u003Cp\u003ESplitting data into train and test sets allows models to be built with the train set and evaluated on the test data. This protects against overfit and yields more accurate error estimates. To use the Dataset Splitter utility, right click the dataset and select \u003Ccode\u003ESPLIT\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EName your \u003Ccode\u003Etrain\u003C/code\u003E and \u003Ccode\u003Etest\u003C/code\u003E splits, then select a split ratio (here we use 0.8).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_02.png\" alt=\"\"\u003E\nFor a time series use case, enter the time column. If your data have predefined folds for k-fold cross validation, enter the fold column. A seed is available for reproducibility. Select the target column \u003Ccode\u003Ebad_loan\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_03.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe data type of the target column determines the splitting algorithm. For classification problems, stratefied random sampling is used. For numeric target columns, simple random sampling is used.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ESAVE\u003C/code\u003E to create the datasets.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_04.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Etrain\u003C/code\u003E dataset has around 31,000 rows and the \u003Ccode\u003Etest\u003C/code\u003E dataset around 8000 rows.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExperiment\u003C/h2\u003E\n","\u003Cp\u003EWe use the term \u003Cem\u003EExperiment\u003C/em\u003E in Driverless AI to refer to the entire feature engineering and model evolution process. Instead of fitting one model, we are fitting many and using a "survival of the fittest" approach to optimize features and model hyperparameters. The result is a combination feature engineering-modeling \u003Cem\u003Epipeline\u003C/em\u003E, which can easily be investigated and promoted into production.\u003C/p\u003E\n","\u003Ch3\u003ESet up an Experiment\u003C/h3\u003E\n","\u003Cp\u003EWe start an experiment from the \u003Ccode\u003EDatasets\u003C/code\u003E view by clicking on the line corresponding to the \u003Ccode\u003Etrain\u003C/code\u003E dataset and selecting \u003Ccode\u003EPREDICT\u003C/code\u003E from the dropdown menu\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_intro_1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis opens the following form for configuring an experiment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe fields are\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E(Optional) Name your experiment. This is especially helpful for leaderboards in \u003Ccode\u003EProjects\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EThe prefilled training dataset.\u003C/li\u003E\u003Cli\u003E(Optional) Select columns to drop from modeling.\u003C/li\u003E\u003Cli\u003E(Optional) Select a validation dataset. Setting this option will enforce a train-validate split throughout the experiment.\u003C/li\u003E\u003Cli\u003E(Recommended) Select the test dataset. You should \u003Cstrong\u003Ealways\u003C/strong\u003E have a holdout test dataset to evaluate your model!\u003C/li\u003E\u003Cli\u003ESelect the target column. This option is flashing so you will not miss it.\u003C/li\u003E\u003Cli\u003E(Optional) Select a column containing fold numbers. This is used where folds for k-fold cross validation have already been defined.\u003C/li\u003E\u003Cli\u003E(Optional) Select weight column.\u003C/li\u003E\u003Cli\u003E(Optional) Select a time column. This switches Driverless AI into a time-series mode, where specialized data, feature engineering, and model settings are enabled.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor our experiment, enter "Baseline" as the display name (#1). Next select the \u003Ccode\u003ETEST DATASET\u003C/code\u003E file \u003Ccode\u003Etest\u003C/code\u003E (#5). The \u003Ccode\u003Edesc\u003C/code\u003E column contains a written explanation from the customer describing the reason for requesting a loan. Although Driverless AI has extensive NLP (natural language processing) capabilities, we omit them in this baseline model. Thus using \u003Ccode\u003EDROPPED COLUMNS\u003C/code\u003E (#3), select \u003Ccode\u003Edesc\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_30.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext select \u003Ccode\u003Ebad_loan\u003C/code\u003E as the \u003Ccode\u003ETARGET COLUMN\u003C/code\u003E (#6). You will have to scroll down, since \u003Ccode\u003Ebad_loan\u003C/code\u003E is the next-to-last variable in the dataset\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_31.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter selecting the target variable, Driverless AI analyzes the data and experimental settings and prefills additional options:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_32.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThese include\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ETarget variable status\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003EACCURACY/TIME/INTERPRETABILITY\u003C/code\u003E dials which range from 1 to 10 and largely determine the recipe for the experiment.\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003ECLASSIFICATION/REPRODUCIBLE/GPUS DISABLED\u003C/code\u003E clickable buttons.\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003ESCORER\u003C/code\u003E used in model building and evaluation.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EEXPERT SETTINGS\u003C/code\u003E for fine control over a vast number of system, model, feature, recipe, and specialty options.\u003C/li\u003E\u003Cli\u003EA detailed settings description.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ELAUNCH EXPERIMENT\u003C/code\u003E to run the experiment defined by dial settings, scorer, and expert settings.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor our experiment,\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe target variable is \u003Ccode\u003Ebool\u003C/code\u003E (Boolean) with 31,184 observations, 4963 of which are equal to 1 (#1). The \u003Ccode\u003ECLASSIFICATION\u003C/code\u003E button (#3) is enabled by default because the target is Boolean.\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003EACCURACY\u003C/code\u003E dial is set to 5. Higher values of accuracy are more computationally intensive. The description under (#6) shows that \u003Ccode\u003EACCURACY\u003C/code\u003E impacts how features are evaluated (model & validation strategy) and what form the final pipeline will take (individual models vs. ensembles and validation strategy).\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003ETIME\u003C/code\u003E dial is set to 4. Higher values of \u003Ccode\u003ETIME\u003C/code\u003E allow for longer feature evolution. \u003Ccode\u003ETIME\u003C/code\u003E levels also include early stopping rules for efficiency.\u003C/li\u003E\u003Cli\u003ENote: Higher values of \u003Ccode\u003EACCURACY\u003C/code\u003E and \u003Ccode\u003ETIME\u003C/code\u003E do not always lead to better predictive models. Model performance should always be evaluated using a holdout test data set.\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003EINTERPRETABILITY\u003C/code\u003E dial ranges from 1 (least interpretable = most complex) to 10 (most interpretable = least complex). \u003Ccode\u003EINTERPRETABILITY\u003C/code\u003E set to 7 or higher enable monotonicity constraints, which significantly increases model understanding.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EClick on the \u003Ccode\u003EREPRODUCIBLE\u003C/code\u003E button to enable reproducibility. This may be important for regulatory reasons or, as in our case, for educational purposes. Also select AUC as the scorer (#4)\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_04.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClicking on \u003Ccode\u003EEXPERT SETTINGS\u003C/code\u003E (#5) exposes an immense array of options and settings\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_06.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis gives the expert data scientist complete control over the Driverless AI experience, including the ability to customize models, feature transformers, scorers, and data using \u003Ccode\u003ECUSTOM RECIPES\u003C/code\u003E. Select \u003Ccode\u003ECANCEL\u003C/code\u003E to exit out of the expert settings screen.\u003C/p\u003E\n","\u003Ch3\u003ERun Experiment\u003C/h3\u003E\n","\u003Cp\u003EBefore launching the experiment, your settings should look like the following.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_10.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ELAUNCH EXPERIMENT\u003C/code\u003E to commence.\nThe Driverless AI UI now includes a descriptive rotating dial in the center with live monitoring displays for model evolution, variable importance, resource usage, and model evaluation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_11.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo get more detailed resource monitoring, go to \u003Ccode\u003ERESOURCES\u003C/code\u003E in the menu and select \u003Ccode\u003ESYSTEM INFO\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_12.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ESystem Info\u003C/code\u003E view shows hardware usage and live activity monitoring of individual CPU cores.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_13.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClicking \u003Ccode\u003ECLOSE\u003C/code\u003E sends us back to the running \u003Ccode\u003EExperiment Baseline\u003C/code\u003E view.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_14.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENote that\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EThe central dial shows 7% completion after 1:06 with 9/56 planned iterations completed.\u003C/li\u003E\u003Cli\u003EThe CPU and memory usage monitor is a simplified version of the \u003Ccode\u003ESystem Info\u003C/code\u003E view we just closed.\u003C/li\u003E\u003Cli\u003EEach dot in the \u003Ccode\u003EITERATION\u003C/code\u003E monitor corresponds to an individual model. The last model evaluated is a LightGBM model with 21 features and an AUC of 0.7316. Moving your mouse over any of the model dots will highlight that model and summary information.\u003C/li\u003E\u003Cli\u003EThe \u003Ccode\u003EVARIABLE IMPORTANCE\u003C/code\u003E display shows the features of the latest model (or the model selected in the \u003Ccode\u003EITERATION DATA\u003C/code\u003E display) and their relative importance.\u003C/li\u003E\u003Cli\u003EBy default, the ROC curve for the selected model and AUC are displayed, but other displays are available: P-R (Precision Recall), Lift, Gains, and K-S (Kolmogorov-Smirnov).\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003ENotifications\u003C/h4\u003E\n","\u003Cp\u003ESelecting \u003Ccode\u003ENotifications\u003C/code\u003E in the \u003Ccode\u003ECPU/MEMORY\u003C/code\u003E section (2) opens important information and discoveries from Driverless AI.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_16.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOurs reports that\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReproducible mode was enabled, along with its implications.\u003C/li\u003E\u003Cli\u003EImbalanced data was detected but imbalanced settings were not enabled. Notifications then indicates the expert settings required to account for imbalance in the data.\u003C/li\u003E\u003Cli\u003EAn ID column was identified and automatically dropped from data.\u003C/li\u003E\u003Cli\u003EAdditional information on scoring during feature and model tuning.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENotification are important to read and understand. The advice in notifications often leads to better models.\u003C/p\u003E\n","\u003Ch4\u003ETechnical logs\u003C/h4\u003E\n","\u003Cp\u003EThe technical data scientist might consider selecting \u003Ccode\u003ELog\u003C/code\u003E in the \u003Ccode\u003ECPU/MEMORY\u003C/code\u003E section. Driverless AI logs its entire process in great detail. Clicking \u003Ccode\u003ELog\u003C/code\u003E opens a system logging window for monitoring live. Logs can be downloaded during or after the experiment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_15.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENearing the conclusion of the experiment\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_20.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Ewe see that the dial is at 100% complete, the elapsed time is approximately 6:30 (while results are reproducible, times are not themselves exactly reproducible), and the experiment is stopping early, needing only 33 of 56 iterations.\u003C/p\u003E\n","\u003Ch3\u003ECompleted Experiment\u003C/h3\u003E\n","\u003Cp\u003EUpon completion, the \u003Ccode\u003EExperiment Baseline\u003C/code\u003E view replaces the spinning dial in the center with a stack of clickable bars\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ESummary\u003C/h4\u003E\n","\u003Cp\u003EThe lower right panel includes an experiment summary, zoomed in below:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_00.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe summary contains information about the experiment settings, its seed, the train, validation, and test data, system (hardware) specifications, features created, models created, timing, and scores. In particular, note that\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E230 features were created but only 28 were used,\u003C/li\u003E\u003Cli\u003Efeature evolution used 35 models,\u003C/li\u003E\u003Cli\u003Efeature tuning used 16 models,\u003C/li\u003E\u003Cli\u003Efinal pipeline training used an additional 8 models.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EImportantly, the MOJO latency timing of 0.13 milliseconds indicates the speed of scoring this model in production.\u003C/p\u003E\n","\u003Ch4\u003EModel Performance\u003C/h4\u003E\n","\u003Cp\u003ESelecting ROC in the lower right replaces the summary with the ROC curve.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_02.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can toggle between \u003Ccode\u003EVALIDATION METRICS\u003C/code\u003E and \u003Ccode\u003ETEST SET METRICS\u003C/code\u003E for this display.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_03.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelecting any point along the curve produces a confusion matrix with additional peformance metrics\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_04.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can view other model performance metrics, including Precision-Recall\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_05.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELift chart\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_06.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGains chart\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_07.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand Kolmogorov-Smirnov\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_08.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExperiment Inspection\u003C/h2\u003E\n","\u003Cp\u003EOnce an experiment is completed, it is important to understand the final model's predictive performance, its features, parameters, and how the features and model combine to make a pipeline.\u003C/p\u003E\n","\u003Ch3\u003EDiagnostics\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EDIAGNOSE MODEL ON NEW DATASET ...\u003C/code\u003E button is used to create extensive diagnostics for a model built in Driverless AI. After clicking the button,\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eselect the dataset used for diagnostics, we will use the \u003Ccode\u003Etest\u003C/code\u003E dataset.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_7.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EDiagnostics\u003C/code\u003E view that is returned is very complete. You can choose from a plethora of \u003Ccode\u003EScores\u003C/code\u003E on the left. And each of the \u003Ccode\u003EMetric Plots\u003C/code\u003E on the right is interactive.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelecting the confusion matrix plot yields\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELikewise, the interactive ROC curve produces\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAutoReport\u003C/h3\u003E\n","\u003Cp\u003EBy default, an automated report is created for each experiment that is run. Download the \u003Ccode\u003EAutoReport\u003C/code\u003E by\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_0.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe document that is created is a very thorough summary of the experiment in the form of a white paper, documenting in detail the data, settings, and methodologies used to create the final pipeline.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis includes detailed information on the features that were engineered and the process for engineering them.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIt also contains validation and test metrics and plots.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor this particular experiment, the AutoReport is a 36-page technically detailed document.\u003C/p\u003E\n","\u003Ch3\u003EPipeline Visualization\u003C/h3\u003E\n","\u003Cp\u003ESelecting the \u003Ccode\u003EVISUALIZE SCORING PIPELINE\u003C/code\u003E button\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_pipeline_1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Ereturns a visual representation of the pipeline\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_pipeline_2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis pipeline is also available in the AutoReport, along with explanatory notes copied below. The pipeline consists of\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E28 total features, both original and engineered.\u003C/li\u003E\u003Cli\u003ETwo LightGBM models created with 4-fold cross validation each.\u003C/li\u003E\u003Cli\u003EA stacked ensemble blending the two LightGBM models.\u003C/li\u003E\u003Cli\u003EThe outputs are probabilities for \u003Ccode\u003Ebad_loan = False\u003C/code\u003E and \u003Ccode\u003Ebad_loan = True\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Interpretability\u003C/h2\u003E\n","\u003Cp\u003EOne of Driverless AI's most important features is the implementation of a host of cutting-edge techniques and methodologies for interpreting and explaining the results of black-box models. In this tutorial, we just highlight some of the MLI features available in Driverless AI without discussing their theoretical underpinnings.\u003C/p\u003E\n","\u003Cp\u003ETo launch MLI from a completed experiment, select the \u003Ccode\u003EINTERPRET THIS MODEL\u003C/code\u003E button\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_00.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe MLI view allows easy navigation through the various interactive plots.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EDashboard\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EDashboard\u003C/code\u003E view displays four useful summary plots\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_02.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA K-LIME (Local Interpretable Model-agnostic Explanations) surrogate model.\u003C/li\u003E\u003Cli\u003EA Decision Tree surrogate model.\u003C/li\u003E\u003Cli\u003EA feature importance plot.\u003C/li\u003E\u003Cli\u003EA PDP (Partial Dependence Plot).\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EEach of these plots are available in a larger format from the main MLI view.\u003C/p\u003E\n","\u003Ch3\u003EFeature Importance\u003C/h3\u003E\n","\u003Cp\u003EOther plots include Feature importance on the transformed features\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_04.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand on the original features\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_05.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EShapley\u003C/h3\u003E\n","\u003Cp\u003EShapley values are also available for the transformed and original features\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_06.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdditional Capabilities\u003C/h3\u003E\n","\u003Cp\u003EThe MLI view provides tools for disparate impact analysis and sensitivity analysis, also called "What If" analysis.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_01.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy the model using Java UDFs\u003C/h2\u003E\n","\u003Ch3\u003EIntroduction\u003C/h3\u003E\n","\u003Cp\u003EThe final model from a Driverless AI experiment can be exported as either a \u003Cstrong\u003EMOJO scoring pipeline\u003C/strong\u003E or a \u003Cstrong\u003EPython scoring pipeline\u003C/strong\u003E. The MOJO scoring pipeline comes with a \u003Ccode\u003Epipeline.mojo\u003C/code\u003E file that can be deployed in any environment that supports Java or C++. There are a myriad of different deployment scenarios for Real-time, Batch or Stream scoring with the \u003Ccode\u003Epipeline.mojo\u003C/code\u003E file. In this tutorial, we deploy the final model as a Snowflake Java UDF.\u003C/p\u003E\n","\u003Ch3\u003EGather Driverless AI artifacts\u003C/h3\u003E\n","\u003Cp\u003EWe need to collect the following components from Driverless AI:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Epipeline.mojo\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Emojo2-runtime.jar\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EH2oDaiScore.jar\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA valid Driverless AI license file. \u003Ccode\u003Elicense.sig\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E(as pointed out in the Prerequisites, a H2O Driverless AI trial license key can be obtained \u003Ca href=\"https://www.h2o.ai/try-driverless-ai/\"\u003Ehere\u003C/a\u003E.)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe first two files we will download from Driverless AI directly. Select \u003Ccode\u003EDOWNLOAD MOJO SCORING PIPELINE\u003C/code\u003E from the \u003Ccode\u003ESTATUS: COMPLETE\u003C/code\u003E buttons\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/09_deploy_01.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eand then \u003Ccode\u003EDOWNLOAD MOJO SCORING PIPELINE\u003C/code\u003E again from the \u003Ccode\u003EMOJO Scoring Pipeline instructions\u003C/code\u003E screen\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/09_deploy_02.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis downloads a file \u003Ccode\u003Emojo.zip\u003C/code\u003E which contains the \u003Ccode\u003Epipeline.mojo\u003C/code\u003E and \u003Ccode\u003Emojo2-runtime.jar\u003C/code\u003E files, along with a number of other files we will not be needing.\u003C/p\u003E\n","\u003Cp\u003EThe next file, \u003Ccode\u003EH2oDaiScore\u003C/code\u003E, is a custom scorer developed by H2O.ai to deploy MOJOs using Snowflake Java UDFs. It can be downloaded from H2O here: \u003Ca href=\"https://s3.amazonaws.com/artifacts.h2o.ai/releases/ai/h2o/dai-snowflake-integration/java-udf/download/index.html\"\u003Ehttps://s3.amazonaws.com/artifacts.h2o.ai/releases/ai/h2o/dai-snowflake-integration/java-udf/download/index.html\u003C/a\u003E. Select the latest release (0.0.7 at the time of this writing). Extract the downloaded \u003Ccode\u003EH2oScore-0.0.7.tgz\u003C/code\u003E file to find \u003Ccode\u003EH2oDaiScore-0.0.7.jar\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ELast, you will need your Driverless AI license file \u003Ccode\u003Elicense.sig\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESetup Snowflake\u003C/h3\u003E\n","\u003Cp\u003EThe first step in creating a Java UDF in Snowflake is to put the 4 Driverless AI artifacts into the table stage, which was created when we created \u003Ccode\u003Eloans\u003C/code\u003E table and uploaded some data in the very beginning.\u003C/p\u003E\n","\u003Cp\u003EIn order to do that, we will need to leverage \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\"\u003ESnowSQL\u003C/a\u003E (Snowflake's CLI tool), which will need to be installed locally so you can put the artifacts on your local computer into the table stage in your Snowflake Cloud.\u003C/p\u003E\n","\u003Cp\u003ETravel to your command line and enter the follow:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Esnowsql\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will be asked for your \u003Ccode\u003EAccount\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003EThis is a part of the unique URL you were given when creating a trial. Here is how the URL is defined (<Account>.snowflakecomputing.com). Enter only the Account portion.\u003C/p\u003E\n","\u003Cp\u003ENext enter your \u003Ccode\u003EUser\u003C/code\u003E:\nand \u003Ccode\u003EPassword\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003EThese are the login name  and password you created after navigating to the unique URL of your Snowflake deployment.\u003C/p\u003E\n","\u003Cp\u003EOnce logged in, you can now execute the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE DATABASE PC_H2O_DB;\nUSE SCHEMA public;\nUSE WAREHOUSE PC_H2O_WH;\nUSE ROLE PC_H2O_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, we can now upload the 4 artifacts:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eput file://{path}/pipeline.mojo @%loans auto_compress=false;\nput file://{path}/license.sig @%loans auto_compress=false;\nput file://{path}/H2oDaiScore-0.0.7.jar @%loans auto_compress=false;\nput file://{path}/mojo2-runtime.jar @%loans auto_compress=false;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote, you will need to change where it says \u003Ccode\u003Epath\u003C/code\u003E in the 'put' commands to path where the files you downloaded are located. This will take 1-2 mins to upload.\u003C/p\u003E\n","\u003Ch3\u003ECreate a Java UDF in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EWe are now ready to actually create the Java UDF via the \u003Ccode\u003ECREATE FUNCTION\u003C/code\u003E statement. To do so, you must provide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ea name for the function and its parameters,\u003C/li\u003E\u003Cli\u003Ethe location in the stage of \u003Ccode\u003Epipeline.mojo\u003C/code\u003E and all other artifacts,\u003C/li\u003E\u003Cli\u003Ethe Java method to be invoked when the Java UDF is called.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe code has been prepared for you. At this point, this can either be run in SnowSQL or back in your GUI session.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE FUNCTION H2OScore_Java(params STRING, rowData ARRAY)\n\n    returns variant language java\n\n    imports = ('@%loans/pipeline.mojo',\n               '@%loans/license.sig',\n               '@%loans/mojo2-runtime.jar',\n               '@%loans/H2oDaiScore-0.0.7.jar'\n               )\n\n    handler = 'h2oDai.H2oDaiScore.h2oDaiScore';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMake predictions using the Java UDF\u003C/h3\u003E\n","\u003Cp\u003EThe syntax for calling a Java UDF in Snowflake is\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT <JAVA_UDF_FUNCTION_NAME>(<JAVA_UDF_FUNCTION_PARAMS>) FROM <TABLE_NAME>;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImporttant:\u003C/strong\u003E  H2O's customer scorer, \u003Ccode\u003EH2oDaiScore.jar\u003C/code\u003E, has a unique feature to autogenerate the SQL command for scoring. Simply call the Java UDF you just created (\u003Ccode\u003EH2OScore_Java\u003C/code\u003E) with the parameter \u003Ccode\u003Esql\u003C/code\u003E set to \u003Ccode\u003Etrue\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor example,\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT H2OScore_Java('Modelname=pipeline.mojo Sql=true', ARRAY_CONSTRUCT());\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Preview\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E"select ROW_NUMBER() OVER (ORDER BY (select 0)) as RowNumber, H2OScore_Java('Modelname=pipeline.mojo', ARRAY_CONSTRUCT(loan_amnt, term, int_rate, installment, emp_length, home_ownership, annual_inc, verification_status, addr_state, dti, delinq_2yrs, inq_last_6mths, pub_rec, revol_bal, revol_util, total_acc)) from <add-table-name>;"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's look at an example using the \u003Ccode\u003EH2OScore_Java\u003C/code\u003E UDF defined above to score our table \u003Ccode\u003Eloans\u003C/code\u003E using \u003Ccode\u003Epipeline.mojo\u003C/code\u003E as follows:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    ROW_NUMBER() OVER (ORDER BY (select 0)) as RowNumber,\n    H2OScore_Java(\n        'Modelname=pipeline.mojo',\n        ARRAY_CONSTRUCT(loan_amnt, term, int_rate, installment, emp_length,\n                        home_ownership, annual_inc, verification_status, addr_state,\n                        dti, delinq_2yrs, inq_last_6mths, pub_rec, revol_bal, revol_util, total_acc)\n    ) AS H2OScore\nFROM loans;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt should take about 7 seconds to score and the results should look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResults Preview\u003C/strong\u003E (first 3 rows)\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERow\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EID\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EH2OScore\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1077501\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.8469023406505585\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1077430\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.5798575133085251\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1077175\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.5994115248322487\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EAnd as they say, that is all folks! We have now scored a model inside Snowflake. What this does is give you the flexibility of Snowflake's Scale Up and Scale Out capabilities to score as much data as you want.\u003C/p\u003E\n","\u003Ch3\u003E(Extra) Easy Deployment using AutoGen\u003C/h3\u003E\n","\u003Cp\u003EA Snowflake Worksheet template to deploy and score DAI MOJOs using Java UDFs can be automatically generated using the H2O REST Server deployment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Ecurl "<ip>:<port>/autogen?name=<model_name>&notebook=snowflake.udf"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor example,\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Ecurl "http://127.0.0.1:8080/autogen?name=pipeline.mojo&notebook=snowflake.udf"\n\u003C/code\u003E\u003C/pre\u003E"],"description":"Build ML models with H2O AutoML on Snowflake for automated feature engineering, model selection, tuning, and production deployment.","title":"AutoML with Snowflake and H2O Driverless AI","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Use Case Overview\n\nH2O Driverless AI is a supervised machine learning platform leveraging the concept of automated machine learning. Supervised machine learning is a method that takes historic data where the response or **target** is known and build relationships between the input variables and the target variable. Driverless AI automates most of difficult supervised machine learning workflow such as feature engineering, model validation, model tuning, model selection, and model deployment. Modeling pipelines, which are produced from H2O Driverless AI, can exported as standalone scoring artifacts to power your AI/ML use case.\n\nThis tutorial presents a quick introduction to the Driverless AI platform via Snowflake Partner Connect.\n\nWe will use a dataset from LendingClub.com to build a classification model to help us predict the likelihood a LendingClub.com borrower will default on their loan. LendingClub.com is an established online loan marketplace that funds personal loans, commercial loans, funding of medical procedures, and other financing needs. The data consist of 25 columns and approximately 39,000 rows, with each row corresponding to a customer. Here is preview of the data:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/00_intro_01.png)\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/00_intro_02.png)\n\nNote that the dataset consist of numerical columns (`loan_amount`, `installment`, `emp_length`, `dti`, etc.), categorical columns (`term`, `home_ownership`, `verification_status`, `purpose`, etc.), and a text column (`desc`). Our target variable is `bad_loan` which is a Boolean with values `True` and `False`, thus this will be a binary classification problem.\n\nWe will use Snowflake and Driverless AI to:\n\n- **Import** the data from Snowflake\n- **Explore** the data using summary descriptive statistics and automated visualizations (AutoViz)\n- **Build** a predictive model using an evolutionary algorithm for automatic feature engineering and model optimization\n- **Measure** the model through diagnostics\n- **Understand** the model through MLI (machine learning interpretability)\n- **Deploy** the model into production in a Snowflake system\n\n### Prerequisites\n\n* A [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) Account deployed in AWS (if you are using an enterprise account through your organization, it is unlikely that you will have the privileges to use the `ACCOUNTADMIN` role, which is required for this lab)\n* A [H2O](https://www.h2o.ai/try-driverless-ai/) trial license key\n* [SnowSQL](https://docs.snowflake.com/en/user-guide/snowsql-install-config.html) installed (Snowflake's CLI tool)\n* Past experience running and executing queries in Snowflake\n* A basic understanding of data science and machine learning concepts\n\n### What You'll Learn\n\n* How to use Snowflake's \"Partner Connect\" to create a Driverless AI instance\n* How to use Driverless AI to build a supervised learning classification model\n* How to deploy the finished model pipeline as a Snowflake Java UDF\n\n\u003C!-- ------------------------ --\u003E\n## Setting up Snowflake\n\nThe first thing you will need to do is download the following .sql file that contains a series of SQL commands we will execute throughout this lab.\n\n\u003Cbutton\u003E\n\n  [Download .sql File](https://snowflake-workshop-lab.s3.amazonaws.com/h2o/Snowflake_H2o_VHOL_guides.sql)\n\u003C/button\u003E\n\nAt this point, log into your Snowflake account and have a clear screen to start working with. If you have just created a free trial account, feel free to minimize or close any hint boxes that are looking to help guide you. These will not be needed for this lab as most of the hints will be covered throughout the remainder of this exercise.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p5.png)\n\nTo ingest our script in the Snowflake UI, navigate to the ellipsis button on the top right hand side of a “New Worksheet” and load our script.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p2.png)\n\nSnowflake provides \"worksheets\" as the spot for you to execute your code. This lab assumes you have already run a few queries in Snowflake before. Therefore, we are going to execute a series of commands quickly, so we get the data in tables and continue to the more interesting part of the lab of building and deploying models. The .sql file that you upload should look like this:\n\n```sql\nUSE ROLE PC_H2O_ROLE;\nUSE DATABASE PC_H2O_DB;\nUSE SCHEMA public;\nUSE WAREHOUSE PC_H2O_WH;\n\nCREATE OR REPLACE TABLE loans (\n    id INTEGER,\n    loan_amnt INTEGER,\n    term String(1024),\n    installment Real,\n    grade String(1024),\n    ...)\n\n...\n```\n\n**Note: before you execute the SQL statements, please proceed to the next section to connect to H2O and launch your Driverless AI instance.**\n\n\n\u003C!-- ------------------------ --\u003E\n## Launching Driverless AI\n\nSnowflake's Partner Connect feature allows you to seamlessly get started with partner tools and manages most of the connection details for you to get up and running as quickly as possible.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p20.png)\n\nGo ahead and click on the \"Partner Connect\" application. This should take you to the following screen where you will see many of the Snowflake partners, and through a simple method of setting up an account and integration, allow you to quickly move data into a partner tool.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p21.png)\n\nTo be able to continue test out partner applications, in our case H2O, we need to promote ourselves to the `ACCOUNTADMIN` role. This is an out of worksheet process, and therefore isn't a command for us to run. We need to do this one manually.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/p22.png)\n\nOnce you have completed this step, go ahead and click on the H2O application. This will present you with a screen to connect to H2O. It will outline a number of Snowflake objects that will be auto-created. For the purposes of this lab, we have already created the snowflake objects that we will need, so you can press \"Connect\" .\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_01.png)\n\nThis creates a partner account which you can immediately `Activate`\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_02.png)\n\nYou next need to accept the H2O Terms and Conditions for the Trial Agreement\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_03.png)\n\nand wait while your H2O Driverless AI instance is configured and launched.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_startup_04.png)\n\n### Driverless AI Interface\n\nYour brand new Driverless AI instance looks like\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/01_intro_0.png)\n\nA summary of the information and views we will cover in this tutorial include:\n\n1. H2O.ai information: This displays the version (Driverless AI 1.9.0.2), the license owner and status, and the current user (H2OAI).\n2. `DATASETS`: A view for importing, listing, and operating on datasets.\n3. `AUTOVIZ`: The Automatic Visualizations of data view.\n4. `EXPERIMENTS`: The view where we build and deploy predictive models.\n5. `DIAGNOSTICS`: Model diagnostics view.\n6. `MLI`: Machine learning interpretability view, information to help us understand our models.\n7. `RESOURCES`: A pull-down menu for accessing system information, clients, help, and other resources.\n\n## Loading dataset and creating a Snowflake table\n\nNow let's setup the database and warehouse in Snowflake, and create a table to use for the lab.\n\nIn the Snowflake worksheet, you have previously loaded a `.sql` script. The SQL commands in this script will import the Lendingclub dataset and create a table called `loans`. This table will be used with H2O Driverless AI to train and deploy a machine learning model.\n\nTo execute the entire .sql code, which contains 9 different statements, all we need to do is click on the \"All Queries\" button next to blue \"run\" button at the top left of the worksheet and then press \"run\". You should see the \"run\" button has a \"(9)\", meaning it will execute all 9 commands in the uploaded file.\n\n\n\u003C!-- ------------------------ --\u003E\n## Import Data from Snowflake\n\nFrom the empty Datasets view, click the `Add Dataset` button and select the `SNOWFLAKE` connector:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_data_1.png)\n\nThis launches the `Make Snowflake Query` form.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_2.png)\nEnter into the form:\n\n* **Database** `PC_H2O_DB`\n* **Warehouse** as `PC_H2O_WH`\n* **Schema** as `PUBLIC`\n* **Name** as `loans.csv`\n* **Username** and **Password** with the credentials you used at signup\n* **File Formatting Parameters** as `FIELD_OPTIONALLY_ENCLOSED_BY = '\"'`\n  \u003E note: the quotation marks are **_single double single_**\n* **SQL Query** `SELECT * FROM LOANS`\n\nThen click the `CLICK TO MAKE QUERY` button. This imports the data into the Driverless AI system.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_5.png)\n\nThe dataset is now available for next steps in Driverless AI\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/02_import_3.png)\n\n\u003C!-- ------------------------ --\u003E\n## Dataset Details\n\nRight click the `loans` dataset to get details.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_0.png)\n\nThe `Dataset Details` view is a quick way to inspect the dataset columns, see their storage type (integer, string, etc.), get summary statistics and distribution plots for each column.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_1.png)\n\nIn more advanced usage, you can edit the data type interactively\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_4.png)\n\nScrolling to the right, inspect the `bad_loans` column, our target variable.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_2.png)\n\nThe target `bad_loans` is Boolean with 38,980 observations and has a mean value of 0.1592. This means that 15.92% of the customers (rows) in this dataset have a loan that was not paid off.\n\nClicking the `DATASET ROWS` button on the upper right yields a spreadsheet format.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/03_details_3.png)\n\nThis is helpful in understanding the layout of the data. A quick inspection of your dataset using `Details` is a good practice that we always recommended.\n\n\u003C!-- ------------------------ --\u003E\n## Visualizing Datasets\n\n`Autoviz` in Driverless AI automatically creates a variety of informative interactive graphs that are designed for understanding the data to be used in building a predictive model. `Autoviz` is unique in that it only shows the graphs that are applicable for your data based on the information in your data.\n\nRight click the dataset name and select `VISUALIZE` to launch AutoViz\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_00.png)\n\nThe available visualizations for the `loans` data are shown below.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_02.png)\n\nSelecting the `SKEWED HISTOGRAMS` section, for example, yields a series of histograms on only the columns that are sufficiently skewed. We show one below for the `credit_length` column.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_03.png)\n\nClicking the left and right navigation arrows allows you to inspect additional variables, ordered by their skewness.\n\nClose the `SKEWED HISTOGRAMS` display and scroll down to see `RECOMMENDATIONS`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_01.png)\n\nSelecting `RECOMMENDATIONS` produces\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/04_autoviz_05.png)\n\nThe philosophy underlying automatic visualizations is to make it easy for the data scientist to quickly understand their data fields, but it does not make decisions for the data scientist.\n\nThere are a number of additional useful graphs that can be navigated to fully understand your data prior to modeling.\n\n\u003C!-- ------------------------ --\u003E\n## Split Data\n\nSplitting data into train and test sets allows models to be built with the train set and evaluated on the test data. This protects against overfit and yields more accurate error estimates. To use the Dataset Splitter utility, right click the dataset and select `SPLIT`\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_01.png)\n\nName your `train` and `test` splits, then select a split ratio (here we use 0.8).\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_02.png)\nFor a time series use case, enter the time column. If your data have predefined folds for k-fold cross validation, enter the fold column. A seed is available for reproducibility. Select the target column `bad_loan`\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_03.png)\n\nThe data type of the target column determines the splitting algorithm. For classification problems, stratefied random sampling is used. For numeric target columns, simple random sampling is used.\n\nClick `SAVE` to create the datasets.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/05_split_04.png)\n\nThe `train` dataset has around 31,000 rows and the `test` dataset around 8000 rows.\n\n\u003C!-- ------------------------ --\u003E\n## Experiment\n\nWe use the term _Experiment_ in Driverless AI to refer to the entire feature engineering and model evolution process. Instead of fitting one model, we are fitting many and using a \"survival of the fittest\" approach to optimize features and model hyperparameters. The result is a combination feature engineering-modeling _pipeline_, which can easily be investigated and promoted into production.\n\n### Set up an Experiment\n\nWe start an experiment from the `Datasets` view by clicking on the line corresponding to the `train` dataset and selecting `PREDICT` from the dropdown menu\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_intro_1.png)\n\nThis opens the following form for configuring an experiment.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_01.png)\n\nThe fields are\n\n1. (Optional) Name your experiment. This is especially helpful for leaderboards in `Projects`.\n2. The prefilled training dataset.\n3. (Optional) Select columns to drop from modeling.\n4. (Optional) Select a validation dataset. Setting this option will enforce a train-validate split throughout the experiment.\n5. (Recommended) Select the test dataset. You should **always** have a holdout test dataset to evaluate your model!\n6. Select the target column. This option is flashing so you will not miss it.\n7. (Optional) Select a column containing fold numbers. This is used where folds for k-fold cross validation have already been defined.\n8. (Optional) Select weight column.\n9. (Optional) Select a time column. This switches Driverless AI into a time-series mode, where specialized data, feature engineering, and model settings are enabled.\n\nFor our experiment, enter \"Baseline\" as the display name (#1). Next select the `TEST DATASET` file `test` (#5). The `desc` column contains a written explanation from the customer describing the reason for requesting a loan. Although Driverless AI has extensive NLP (natural language processing) capabilities, we omit them in this baseline model. Thus using `DROPPED COLUMNS` (#3), select `desc`:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_30.png)\n\nNext select `bad_loan` as the `TARGET COLUMN` (#6). You will have to scroll down, since `bad_loan` is the next-to-last variable in the dataset\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_31.png)\n\nAfter selecting the target variable, Driverless AI analyzes the data and experimental settings and prefills additional options:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_32.png)\n\nThese include\n\n1. Target variable status\n2. The `ACCURACY/TIME/INTERPRETABILITY` dials which range from 1 to 10 and largely determine the recipe for the experiment.\n3. The `CLASSIFICATION/REPRODUCIBLE/GPUS DISABLED` clickable buttons.\n4. The `SCORER` used in model building and evaluation.\n5. `EXPERT SETTINGS` for fine control over a vast number of system, model, feature, recipe, and specialty options.\n6. A detailed settings description.\n7. `LAUNCH EXPERIMENT` to run the experiment defined by dial settings, scorer, and expert settings.\n\nFor our experiment,\n\n* The target variable is `bool` (Boolean) with 31,184 observations, 4963 of which are equal to 1 (#1). The `CLASSIFICATION` button (#3) is enabled by default because the target is Boolean.\n* The `ACCURACY` dial is set to 5. Higher values of accuracy are more computationally intensive. The description under (#6) shows that `ACCURACY` impacts how features are evaluated (model & validation strategy) and what form the final pipeline will take (individual models vs. ensembles and validation strategy).\n* The `TIME` dial is set to 4. Higher values of `TIME` allow for longer feature evolution. `TIME` levels also include early stopping rules for efficiency.\n* Note: Higher values of `ACCURACY` and `TIME` do not always lead to better predictive models. Model performance should always be evaluated using a holdout test data set.\n* The `INTERPRETABILITY` dial ranges from 1 (least interpretable = most complex) to 10 (most interpretable = least complex). `INTERPRETABILITY` set to 7 or higher enable monotonicity constraints, which significantly increases model understanding.\n\n\nClick on the `REPRODUCIBLE` button to enable reproducibility. This may be important for regulatory reasons or, as in our case, for educational purposes. Also select AUC as the scorer (#4)\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_04.png)\n\nClicking on `EXPERT SETTINGS` (#5) exposes an immense array of options and settings\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_setup_06.png)\n\nThis gives the expert data scientist complete control over the Driverless AI experience, including the ability to customize models, feature transformers, scorers, and data using `CUSTOM RECIPES`. Select `CANCEL` to exit out of the expert settings screen.\n\n### Run Experiment\n\nBefore launching the experiment, your settings should look like the following.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_10.png)\n\nClick `LAUNCH EXPERIMENT` to commence.\nThe Driverless AI UI now includes a descriptive rotating dial in the center with live monitoring displays for model evolution, variable importance, resource usage, and model evaluation.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_11.png)\n\nTo get more detailed resource monitoring, go to `RESOURCES` in the menu and select `SYSTEM INFO`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_12.png)\n\nThe `System Info` view shows hardware usage and live activity monitoring of individual CPU cores.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_13.png)\n\nClicking `CLOSE` sends us back to the running `Experiment Baseline` view.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_14.png)\n\nNote that\n\n1. The central dial shows 7% completion after 1:06 with 9/56 planned iterations completed.\n2. The CPU and memory usage monitor is a simplified version of the `System Info` view we just closed.\n3. Each dot in the `ITERATION` monitor corresponds to an individual model. The last model evaluated is a LightGBM model with 21 features and an AUC of 0.7316. Moving your mouse over any of the model dots will highlight that model and summary information.\n4. The `VARIABLE IMPORTANCE` display shows the features of the latest model (or the model selected in the `ITERATION DATA` display) and their relative importance.\n5. By default, the ROC curve for the selected model and AUC are displayed, but other displays are available: P-R (Precision Recall), Lift, Gains, and K-S (Kolmogorov-Smirnov).\n\n#### Notifications\n\nSelecting `Notifications` in the `CPU/MEMORY` section (2) opens important information and discoveries from Driverless AI.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_16.png)\n\nOurs reports that\n\n* Reproducible mode was enabled, along with its implications.\n* Imbalanced data was detected but imbalanced settings were not enabled. Notifications then indicates the expert settings required to account for imbalance in the data.\n* An ID column was identified and automatically dropped from data.\n* Additional information on scoring during feature and model tuning.\n\nNotification are important to read and understand. The advice in notifications often leads to better models.\n\n#### Technical logs\n\nThe technical data scientist might consider selecting `Log` in the `CPU/MEMORY` section. Driverless AI logs its entire process in great detail. Clicking `Log` opens a system logging window for monitoring live. Logs can be downloaded during or after the experiment.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_15.png)\n\nNearing the conclusion of the experiment\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_run_20.png)\n\nwe see that the dial is at 100% complete, the elapsed time is approximately 6:30 (while results are reproducible, times are not themselves exactly reproducible), and the experiment is stopping early, needing only 33 of 56 iterations.\n\n### Completed Experiment\n\nUpon completion, the `Experiment Baseline` view replaces the spinning dial in the center with a stack of clickable bars\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_01.png)\n\n#### Summary\n\nThe lower right panel includes an experiment summary, zoomed in below:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_00.png)\n\nThe summary contains information about the experiment settings, its seed, the train, validation, and test data, system (hardware) specifications, features created, models created, timing, and scores. In particular, note that\n\n* 230 features were created but only 28 were used,\n* feature evolution used 35 models,\n* feature tuning used 16 models,\n* final pipeline training used an additional 8 models.\n\nImportantly, the MOJO latency timing of 0.13 milliseconds indicates the speed of scoring this model in production.\n\n#### Model Performance\n\nSelecting ROC in the lower right replaces the summary with the ROC curve.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_02.png)\n\nYou can toggle between `VALIDATION METRICS` and `TEST SET METRICS` for this display.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_03.png)\n\nSelecting any point along the curve produces a confusion matrix with additional peformance metrics\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_04.png)\n\nYou can view other model performance metrics, including Precision-Recall\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_05.png)\n\nLift chart\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_06.png)\n\nGains chart\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_07.png)\n\nand Kolmogorov-Smirnov\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/06_complete_08.png)\n\n\u003C!-- ------------------------ --\u003E\n## Experiment Inspection\n\nOnce an experiment is completed, it is important to understand the final model's predictive performance, its features, parameters, and how the features and model combine to make a pipeline.\n\n### Diagnostics\n\nThe `DIAGNOSE MODEL ON NEW DATASET ...` button is used to create extensive diagnostics for a model built in Driverless AI. After clicking the button,\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_0.png)\n\nselect the dataset used for diagnostics, we will use the `test` dataset.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_7.png)\n\nThe `Diagnostics` view that is returned is very complete. You can choose from a plethora of `Scores` on the left. And each of the `Metric Plots` on the right is interactive.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_2.png)\n\nSelecting the confusion matrix plot yields\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_3.png)\n\nLikewise, the interactive ROC curve produces\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_diagnostics_4.png)\n\n### AutoReport\n\nBy default, an automated report is created for each experiment that is run. Download the `AutoReport` by\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_0.png)\n\nThe document that is created is a very thorough summary of the experiment in the form of a white paper, documenting in detail the data, settings, and methodologies used to create the final pipeline.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_1.png)\n\nThis includes detailed information on the features that were engineered and the process for engineering them.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_2.png)\n\nIt also contains validation and test metrics and plots.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_autodoc_3.png)\n\nFor this particular experiment, the AutoReport is a 36-page technically detailed document.\n\n### Pipeline Visualization\n\nSelecting the `VISUALIZE SCORING PIPELINE` button\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_pipeline_1.png)\n\nreturns a visual representation of the pipeline\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/07_pipeline_2.png)\n\nThis pipeline is also available in the AutoReport, along with explanatory notes copied below. The pipeline consists of\n\n* 28 total features, both original and engineered.\n* Two LightGBM models created with 4-fold cross validation each.\n* A stacked ensemble blending the two LightGBM models.\n* The outputs are probabilities for `bad_loan = False` and `bad_loan = True`.\n\n\u003C!-- ------------------------ --\u003E\n## Model Interpretability\n\nOne of Driverless AI's most important features is the implementation of a host of cutting-edge techniques and methodologies for interpreting and explaining the results of black-box models. In this tutorial, we just highlight some of the MLI features available in Driverless AI without discussing their theoretical underpinnings.\n\n\nTo launch MLI from a completed experiment, select the `INTERPRET THIS MODEL` button\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_00.png)\n\nThe MLI view allows easy navigation through the various interactive plots.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_01.png)\n\n### Dashboard\n\nThe `Dashboard` view displays four useful summary plots\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_02.png)\n\n1. A K-LIME (Local Interpretable Model-agnostic Explanations) surrogate model.\n2. A Decision Tree surrogate model.\n3. A feature importance plot.\n4. A PDP (Partial Dependence Plot).\n\nEach of these plots are available in a larger format from the main MLI view.\n\n### Feature Importance\n\nOther plots include Feature importance on the transformed features\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_04.png)\n\nand on the original features\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_05.png)\n\n### Shapley\n\nShapley values are also available for the transformed and original features\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_06.png)\n\n### Additional Capabilities\n\nThe MLI view provides tools for disparate impact analysis and sensitivity analysis, also called \"What If\" analysis.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/08_mli_01.png)\n\n\u003C!-- ------------------------ --\u003E\n## Deploy the model using Java UDFs\n\n### Introduction\n\nThe final model from a Driverless AI experiment can be exported as either a **MOJO scoring pipeline** or a **Python scoring pipeline**. The MOJO scoring pipeline comes with a `pipeline.mojo` file that can be deployed in any environment that supports Java or C++. There are a myriad of different deployment scenarios for Real-time, Batch or Stream scoring with the `pipeline.mojo` file. In this tutorial, we deploy the final model as a Snowflake Java UDF.\n\n### Gather Driverless AI artifacts\n\nWe need to collect the following components from Driverless AI:\n\n- `pipeline.mojo`\n- `mojo2-runtime.jar`\n- `H2oDaiScore.jar`\n- A valid Driverless AI license file. `license.sig`\n- (as pointed out in the Prerequisites, a H2O Driverless AI trial license key can be obtained [here](https://www.h2o.ai/try-driverless-ai/).)\n\nThe first two files we will download from Driverless AI directly. Select `DOWNLOAD MOJO SCORING PIPELINE` from the `STATUS: COMPLETE` buttons\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/09_deploy_01.png)\n\nand then `DOWNLOAD MOJO SCORING PIPELINE` again from the `MOJO Scoring Pipeline instructions` screen\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-h2o/09_deploy_02.png)\n\nThis downloads a file `mojo.zip` which contains the `pipeline.mojo` and `mojo2-runtime.jar` files, along with a number of other files we will not be needing.\n\nThe next file, `H2oDaiScore`, is a custom scorer developed by H2O.ai to deploy MOJOs using Snowflake Java UDFs. It can be downloaded from H2O here: [https://s3.amazonaws.com/artifacts.h2o.ai/releases/ai/h2o/dai-snowflake-integration/java-udf/download/index.html](https://s3.amazonaws.com/artifacts.h2o.ai/releases/ai/h2o/dai-snowflake-integration/java-udf/download/index.html). Select the latest release (0.0.7 at the time of this writing). Extract the downloaded `H2oScore-0.0.7.tgz` file to find `H2oDaiScore-0.0.7.jar`.\n\nLast, you will need your Driverless AI license file `license.sig`.\n\n### Setup Snowflake\n\nThe first step in creating a Java UDF in Snowflake is to put the 4 Driverless AI artifacts into the table stage, which was created when we created `loans` table and uploaded some data in the very beginning.\n\nIn order to do that, we will need to leverage [SnowSQL](https://docs.snowflake.com/en/user-guide/snowsql-install-config.html) (Snowflake's CLI tool), which will need to be installed locally so you can put the artifacts on your local computer into the table stage in your Snowflake Cloud.\n\nTravel to your command line and enter the follow:\n\n```\nsnowsql\n```\nYou will be asked for your `Account`:\n\nThis is a part of the unique URL you were given when creating a trial. Here is how the URL is defined (\u003CAccount\u003E.snowflakecomputing.com). Enter only the Account portion.\n\nNext enter your `User`:\nand `Password`:\n\nThese are the login name  and password you created after navigating to the unique URL of your Snowflake deployment.\n\nOnce logged in, you can now execute the following:\n```\nUSE DATABASE PC_H2O_DB;\nUSE SCHEMA public;\nUSE WAREHOUSE PC_H2O_WH;\nUSE ROLE PC_H2O_ROLE;\n```\n\nFinally, we can now upload the 4 artifacts:\n\n```\nput file://{path}/pipeline.mojo @%loans auto_compress=false;\nput file://{path}/license.sig @%loans auto_compress=false;\nput file://{path}/H2oDaiScore-0.0.7.jar @%loans auto_compress=false;\nput file://{path}/mojo2-runtime.jar @%loans auto_compress=false;\n```\n\nNote, you will need to change where it says `path` in the 'put' commands to path where the files you downloaded are located. This will take 1-2 mins to upload.\n\n### Create a Java UDF in Snowflake\n\nWe are now ready to actually create the Java UDF via the `CREATE FUNCTION` statement. To do so, you must provide:\n\n- a name for the function and its parameters,\n- the location in the stage of `pipeline.mojo` and all other artifacts,\n- the Java method to be invoked when the Java UDF is called.\n  \n\nThe code has been prepared for you. At this point, this can either be run in SnowSQL or back in your GUI session.\n\n``` sql\nCREATE FUNCTION H2OScore_Java(params STRING, rowData ARRAY)\n\n    returns variant language java\n\n    imports = ('@%loans/pipeline.mojo',\n               '@%loans/license.sig',\n               '@%loans/mojo2-runtime.jar',\n               '@%loans/H2oDaiScore-0.0.7.jar'\n               )\n\n    handler = 'h2oDai.H2oDaiScore.h2oDaiScore';\n```\n\n\n### Make predictions using the Java UDF\n\nThe syntax for calling a Java UDF in Snowflake is\n\n``` sql\nSELECT \u003CJAVA_UDF_FUNCTION_NAME\u003E(\u003CJAVA_UDF_FUNCTION_PARAMS\u003E) FROM \u003CTABLE_NAME\u003E;\n```\n  \n**Importtant:**  H2O's customer scorer, `H2oDaiScore.jar`, has a unique feature to autogenerate the SQL command for scoring. Simply call the Java UDF you just created (`H2OScore_Java`) with the parameter `sql` set to `true`.\n\nFor example,\n  \n  ```sql\n  SELECT H2OScore_Java('Modelname=pipeline.mojo Sql=true', ARRAY_CONSTRUCT());\n \n  ```\n\n  **Results Preview**\n  \n  ```sql\n  \"select ROW_NUMBER() OVER (ORDER BY (select 0)) as RowNumber, H2OScore_Java('Modelname=pipeline.mojo', ARRAY_CONSTRUCT(loan_amnt, term, int_rate, installment, emp_length, home_ownership, annual_inc, verification_status, addr_state, dti, delinq_2yrs, inq_last_6mths, pub_rec, revol_bal, revol_util, total_acc)) from \u003Cadd-table-name\u003E;\"\n  ```\n  \nNow let's look at an example using the `H2OScore_Java` UDF defined above to score our table `loans` using `pipeline.mojo` as follows:\n\n``` sql\nSELECT\n    ROW_NUMBER() OVER (ORDER BY (select 0)) as RowNumber,\n    H2OScore_Java(\n        'Modelname=pipeline.mojo',\n        ARRAY_CONSTRUCT(loan_amnt, term, int_rate, installment, emp_length,\n                        home_ownership, annual_inc, verification_status, addr_state,\n                        dti, delinq_2yrs, inq_last_6mths, pub_rec, revol_bal, revol_util, total_acc)\n    ) AS H2OScore\nFROM loans;\n```\n\nIt should take about 7 seconds to score and the results should look like this:\n\n**Results Preview** (first 3 rows)\n\n| Row | ID      | H2OScore           |\n| --- | ------- | ------------------ |\n| 1   | 1077501 | 0.8469023406505585 |\n| 2   | 1077430 | 0.5798575133085251 |\n| 3   | 1077175 | 0.5994115248322487 |\n\nAnd as they say, that is all folks! We have now scored a model inside Snowflake. What this does is give you the flexibility of Snowflake's Scale Up and Scale Out capabilities to score as much data as you want.\n\n### (Extra) Easy Deployment using AutoGen\n\nA Snowflake Worksheet template to deploy and score DAI MOJOs using Java UDFs can be automatically generated using the H2O REST Server deployment:\n\n``` sh\ncurl \"\u003Cip\u003E:\u003Cport\u003E/autogen?name=\u003Cmodel_name\u003E&notebook=snowflake.udf\"\n```\n\nFor example,\n\n```sh\ncurl \"http://127.0.0.1:8080/autogen?name=pipeline.mojo&notebook=snowflake.udf\"\n```\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a25fd013c3","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":{"layout":"SIMPLE","id":"container-4716a42a23",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a1654c0b98","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-5c8c3dc0a5","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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Case Overview\u003C/h2\u003E","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E","\u003Ch2\u003ELaunching Driverless AI\u003C/h2\u003E","\u003Ch2\u003ELoading dataset and creating a Snowflake table\u003C/h2\u003E","\u003Ch2\u003EImport Data from Snowflake\u003C/h2\u003E","\u003Ch2\u003EDataset Details\u003C/h2\u003E","\u003Ch2\u003EVisualizing Datasets\u003C/h2\u003E","\u003Ch2\u003ESplit Data\u003C/h2\u003E","\u003Ch2\u003EExperiment\u003C/h2\u003E","\u003Ch2\u003EExperiment Inspection\u003C/h2\u003E","\u003Ch2\u003EModel Interpretability\u003C/h2\u003E","\u003Ch2\u003EDeploy the model using Java 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