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2024-07-09\u003C/p\u003E\n<!-- The above name is what appears on the website and is searchable. -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis QuickStart will guide you through an end-to-end example of utilizing Snowflake's new machine learning features.\u003C/p\u003E\n","\u003Cp\u003EOur focus will be on creating a machine learning-driven price prediction tool, integrating the perspectives of both a business analyst and a data scientist using Snowflake and Sigma.\u003C/p\u003E\n","\u003Cp\u003EWe will demonstrate how Sigma enables the execution of complex commands in Snowflake, and displays results in a format similar to a spreadsheet interface.\u003C/p\u003E\n","\u003Cp\u003EThis approach not only maintains data security within Snowflake, but also broadens accessibility to users without SQL expertise.\u003C/p\u003E\n","\u003Cp\u003EIn our example, we'll analyze historic food truck sales data. We aim to develop a model identifying key drivers of high sales in the past and explore how a business analyst can leverage this model for informed decision-making. The analyst will be able to collaborate with the data scientist all from a sigma workbook.\u003C/p\u003E\n","\u003Ch3\u003ETarget Audience\u003C/h3\u003E\n","\u003Cp\u003EAnyone who is interested in learning how to easily leverage the power of Snowflakes machine learning features, by using Sigma.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EIn this lab you will be creating a machine learning-driven price prediction tool, integrating the perspectives of both a business analyst and a data scientist using Snowflake and Sigma.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to explore and build a dataset for training a model\u003C/li\u003E\u003Cli\u003EHow to build a machine learning model using Snowpark ML\u003C/li\u003E\u003Cli\u003EHow to register a model in the Snowpark Model Registry\u003C/li\u003E\u003Cli\u003EHow to expose the model to business users in Sigma\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003EThanks for signing up for Snowflake & Sigma’s Hands-On Lab, “Develop a Predictive Model Using Snowflake and Sigma”. To ensure that you can follow along during the lab, please complete the work outlined below prior to the start of the lab.\u003C/p\u003E\n","\u003Cp\u003E1: Sign up for a Snowflake Trial - You can sign up for a 30-day free trial of Snowflake \u003Ca href=\"https://signup.snowflake.com/\"\u003Ehere.\u003C/a\u003E Even if you have a login on an existing Snowflake account, you should still create a new Snowflake account, as you’ll be asked to utilize the ACCOUNTADMIN role for some steps below.\u003C/p\u003E\n","\u003Cp\u003E2: Accept Anaconda Terms - Follow \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-setup#label-notebooks-anaconda-terms\"\u003Ethese instructions\u003C/a\u003E to accept the Anaconda Terms to import Python libraries in Snowflake. (For this lab, you will need to import the \u003Ca href=\"https://pypi.org/project/snowflake-ml-python/\"\u003Esnowflake-ml-python\u003C/a\u003E package in a Snowflake Notebook.)\u003C/p\u003E\n","\u003Cp\u003E3: Sign Up for a Sigma Trial - From Snowflake, in Snowsight, navigate to \u003Ccode\u003EData Product\u003C/code\u003Es >> \u003Ccode\u003EPartner Connect\u003C/code\u003E, then search for \u003Ccode\u003ESigma\u003C/code\u003E and follow the instructions to sign up for a free trial. When signing up for a Sigma trial via Partner Connect, relevant Snowflake objects, such as a database (PC_SIGMA_DB), a compute warehouse (PC_SIGMA_WH), a role (PC_SIGMA_ROLE), and a user (PC_SIGMA_USER) for working with Snowflake data in Sigma. Furthermore, Partner Connect will automatically create a Snowflake connection for you in Sigma. (Snowflake PC_SIGMA_WH).\u003C/p\u003E\n","\u003Cp\u003EOnce you are done configuring partner connect, or if you already have a Sigma account (or \u003Ca href=\"https://www.sigmacomputing.com/free-trial\"\u003Ecreated a Sigma trial manually\u003C/a\u003E), and did not use Partner Connect to create these Snowflake objects, you can run the following commands in a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-in-sf-web-interface\"\u003ESnowsight Worksheet\u003C/a\u003E to configure your HOL environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-code\"\u003EUSE ROLE SYSADMIN;\nCREATE DATABASE IF NOT EXISTS PC_SIGMA_DB;\nCREATE WAREHOUSE IF NOT EXISTS PC_SIGMA_WH\n\n    WAREHOUSE_SIZE='XSMALL'\n    INITIALLY_SUSPENDED=TRUE\n    AUTO_SUSPEND=120;\n\nUSE ROLE SECURITYADMIN;\n\nCREATE ROLE IF NOT EXISTS PC_SIGMA_ROLE;\n\nGRANT ALL ON DATABASE PC_SIGMA_DB TO ROLE PC_SIGMA_ROLE;\nGRANT USAGE ON WAREHOUSE PC_SIGMA_WH TO ROLE PC_SIGMA_ROLE;\n\nGRANT ROLE PC_SIGMA_ROLE TO ROLE SYSADMIN;\nGRANT ROLE PC_SIGMA_ROLE TO USER <your-snowflake-user>;\n\n\nUSE ROLE SYSADMIN;\nCREATE DATABASE IF NOT EXISTS SIGMA_INTERNAL;\nCREATE SCHEMA IF NOT EXISTS SIGMA_INTERNAL.WRITEBACK;\n\n\nUSE ROLE SECURITYADMIN;\nGRANT ALL ON DATABASE SIGMA_INTERNAL TO ROLE PC_SIGMA_ROLE;\nGRANT ALL ON SCHEMA SIGMA_INTERNAL.WRITEBACK TO ROLE PC_SIGMA_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E4: The following notebook has been provided for you to load into your Snowflake interface.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/notebooks/notebook_app.ipynb\"\u003EDownload Snowflake Notebook\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/pm1.png\" alt=\"assets/pm1.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUpon import, Snowflake will prompt you for a database and schema to be used in the notebook. You can use any database/schema that the Sigma Service Role has access to.\u003C/p\u003E\n","\u003Cp\u003E<ul>\n<li>A Sigma instance that has a connection established to your own Snowflake instance.</li>\n<li>Sigma Write-back is enabled to your Snowflake environment</li>\n<li>A snowflake role with Usage rights on a Snowflake schema that the Sigma Service Role has access to, as well as to the writeback location.</li>\n</ul>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EWe're starting from scratch with a blank Sigma workbook. I'll first load our sales data from Snowflake. If you have sales data in Snowflake, we can directly connect to it from Sigma. In this case, we don’t have that data in Snowflake, so we’ll need to upload it. Fortunately, that’s easy to do in Sigma. Let's upload a CSV file of shift sales from the city of Seattle.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/SHIFT_SALES.csv\"\u003EDownload required CSV file\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003E1: Download the CSV file from this \u003Ca href=\"https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/SHIFT_SALES.csv\"\u003ESigma hosted location:\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E2: From the Sigma home page click the "Create New" button in the top left corner and select "Workbook".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml1.png\" alt=\"assets/ml1.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E3: Now that you are in the Workbook, let's start by saving it with the name "ML Shift Sales" by clicking "Save As" in the top right.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml3.png\" alt=\"assets/ml3.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E4: Add a new table, and then select Upload CSV as an option\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml4.png\" alt=\"assets/ml4.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E5: Make sure your instance is selected in the connection dropdown (NOT the Sigma Sample Database), and then drag your downloaded file into the upload area and then press save in the upper right to upload the table to your Snowflake instance and view the table in your sigma workbook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml5.png\" alt=\"assets/ml5.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ESigma Data Exploration\u003C/h2\u003E\n","\u003Cp\u003E1: Shift sales will be of primary importance for this QuickStart, so let’s adjust adjust its format to \u003Ccode\u003Ecurrency\u003C/code\u003E at the start of our analysis. Select the \u003Ccode\u003EShift Sales\u003C/code\u003E column, and use the buttons next to the formula bar to adjust the format:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml6.png\" alt=\"assets/ml6.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E2: We don’t know what variables impact sales, but it’s a safe bet to start with time having some kind of impact. Let’s make a visual to explore this. Create a \u003Ccode\u003EChild element\u003C/code\u003E visual:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml7.png\" alt=\"assets/ml7.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDrag \u003Ccode\u003EDate\u003C/code\u003E and \u003Ccode\u003EShift Sales\u003C/code\u003E columns to the \u003Ccode\u003EX\u003C/code\u003E and \u003Ccode\u003EY axis\u003C/code\u003E, respectively:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml8.png\" alt=\"assets/ml8.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E3: We can see that Sigma automatically aggregates our data to the \u003Ccode\u003EDay level\u003C/code\u003E and applies a \u003Ccode\u003ESum\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ELets adjust this to a monthly aggregation to "quiet out" some of the noise.\u003C/p\u003E\n","\u003Cp\u003EYou can adjust the formula directly in the formula bar:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml9.png\" alt=\"assets/ml9.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E4: We can see the seasonality of the sales around each January, and we can isolate this further to confirm that suspicion.\u003C/p\u003E\n","\u003Cp\u003EWe will switch the formula to a \u003Ca href=\"https://help.sigmacomputing.com/docs/datepart\"\u003Edatepart() function\u003C/a\u003E, and see that the first 3 months do indeed have the highest sales:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml10.png\" alt=\"assets/ml10.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E5: The second factor that we think may play a role in the sales is the shift that the sales took place in.\u003C/p\u003E\n","\u003Cp\u003EWe can easily add that to our visual, and then switch to a “No Stacking” bar chart, to see the differences between AM and PM shifts:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml11.png\" alt=\"assets/ml11.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E6: The third factor that we think may play a role is the weekday that the sales took place on. This is a very similar question to our monthly analysis.\u003C/p\u003E\n","\u003Cp\u003EWe can duplicate the table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml12.png\" alt=\"assets/ml12.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDrag it next to our first chart:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml13.png\" alt=\"assets/ml13.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd then adjust the \u003Ccode\u003EDatePart() function\u003C/code\u003E to use \u003Ccode\u003Eweekday\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EJust like in the months, we can see that certain weekdays definitely return greater sales:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml14.png\" alt=\"assets/ml14.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ECreate a Dataset for Modeling\u003C/h2\u003E\n","\u003Cp\u003ENow that we have identified Month Number, Weekday, and Shift as potential predictors for shift sales, let’s prepare a dataset with these variables for our data scientist. In Sigma, this may be something that a data scientist does in the existing workbook, or the analyst can prepare it with guidance. There is lots of room for collaboration through \u003Ca href=\"https://help.sigmacomputing.com/docs/workbook-collaboration-with-live-edit\"\u003Elive edit\u003C/a\u003E and our \u003Ca href=\"https://help.sigmacomputing.com/docs/workbook-comments\"\u003Ecomment feature.\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E1: Create a child table of our shift sales table and drag it to the bottom of our workbook:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml15.png\" alt=\"assets/ml15.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E2: For our training set, we want the data to be filtered before a certain date so that we can then assess shifts after that date in the test set.\u003C/p\u003E\n","\u003Cp\u003EWe right click the \u003Ccode\u003EDate\u003C/code\u003E column, and then \u003Ccode\u003Efilter\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml16.png\" alt=\"assets/ml16.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESet the filter to \u003Ccode\u003EBetween\u003C/code\u003E the dates \u003Ccode\u003E1/1/2020 and 12/31/2022\u003C/code\u003E, to get 3 years of training data:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml17.png\" alt=\"assets/ml17.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen, rename this table to \u003Ccode\u003ETrain\u003C/code\u003E by double-clicking the title, to edit it.\u003C/p\u003E\n","\u003Cp\u003E3: Now we need to create the columns that we found to drive Sales.\u003C/p\u003E\n","\u003Cp\u003EAdd a column:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml18.png\" alt=\"assets/ml18.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDefine it as \u003Ccode\u003EDatePart(“month”, [Date])\u003C/code\u003E, so that we get the month number.\u003C/p\u003E\n","\u003Cp\u003E4: We can easily duplicate this column to make a weekday column as well.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EDuplicate\u003C/code\u003E the column and then change \u003Ccode\u003Emonth\u003C/code\u003E to \u003Ccode\u003Eweekday\u003C/code\u003E in the formula:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml19.png\" alt=\"assets/ml19.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E5: Finally, your data scientist may want you to encode categorical data into numerical values.\u003C/p\u003E\n","\u003Cp\u003EWe can easily do this with Sigma using a formula definition.\u003C/p\u003E\n","\u003Cp\u003EAdd a new column, define it's function as \u003Ccode\u003EIf([Shift] = "AM", 1, 0)\u003C/code\u003E, and then rename it to \u003Ccode\u003EEncoded Shift\u003C/code\u003E.:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml20.png\" alt=\"assets/ml20.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E6: Now, we need to repeat all the steps to make a Test table....\u003Cstrong\u003EJust kidding!!\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EAll we need to do is \u003Ccode\u003Eduplicate\u003C/code\u003E the table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml21.png\" alt=\"assets/ml21.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAdjust the \u003Ccode\u003Edate filter\u003C/code\u003E so that it gives us values \u003Ccode\u003Eon or after 1/1/2023\u003C/code\u003E, and then rename the new table to \u003Ccode\u003ETest\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml22.png\" alt=\"assets/ml22.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E7: Finally, we can make all this work available in your Snowflake Write-back schema by creating a warehouse view from the Train table.\u003C/p\u003E\n","\u003Cp\u003EWe recommend calling it \u003Ccode\u003ETrain\u003C/code\u003E, but you can name it anything you’d like:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml23.png\" alt=\"assets/ml23.png\"\u003E<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml24.png\" alt=\"assets/ml24.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<aside class="negative">\n<strong>NOTE:</strong><br> Note that this will get us a fully qualified name that our data scientist can use in their programming.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E8: Repeat this for \u003Ccode\u003ETest\u003C/code\u003E to create a warehouse view for the Test table.\u003C/p\u003E\n","\u003Cp\u003E9: Publish the workbook to create the warehouse views!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003ESnowflake Programming\u003C/h2\u003E\n","\u003Cp\u003EWe can now let our data scientist know that TASTY_BITES_TRAIN is ready for them to train their model on Month, Weekday, and Shift. The Data Scientist can now begin their work in the Snowflake Notebook that was downloaded in the beginning section of this hands on lab.\u003C/p\u003E\n","\u003Cp\u003EYou can run those chunks one at a time and read their notations in the notebook, as well as see the general steps outlined below:\u003C/p\u003E\n","\u003Ch3\u003E1: Create a Model Registry\u003C/h3\u003E\n","\u003Cp\u003EThis is created in your database that you will use to store this and future models.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.registry import Registry\n\nsession.sql("CREATE SCHEMA IF NOT EXISTS ML_REGISTRY").collect()\n\nreg = Registry(session, database_name="SE_DEMO_DB", schema_name="ML_REGISTRY")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E2: Snowpark ML Functions\u003C/h3\u003E\n","\u003Cp\u003EThe new Snowpark ML functions make it super easy to train open-source models on optimized and scalable Snowflake compute. We can set up a code block that allows us to easily train a decision tree regression model with just a few lines. You will use the warehouse view locations that you generated in your sigma workbook in the session table calls below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.modeling.tree import DecisionTreeRegressor\n\n# Specify inputs\ntraining_table = session.table("[USE WAREHOUSE VIEW LOCATION FOR TRAIN]")\ntesting_table = session.table("[USE WAREHOUSE VIEW LOCATION FOR TEST]")\n\n# Input my Analyst's ideas for features\nfeature_cols = [\n    "MONTH_OF_DATE",\n    "WEEKDAY_OF_DATE",\n    "ENCODED_SHIFT",\n]\ntarget_col = "SHIFT_SALES"\n\nmy_model = DecisionTreeRegressor()\nmy_model.set_input_cols(feature_cols)\nmy_model.set_label_cols(target_col)\nmy_model.set_output_cols("PRED_" + target_col)\n\nmy_model.fit(training_table)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E3: Create Governed Metrics\u003C/h3\u003E\n","\u003Cp\u003ESnowflake also offers a large library of metrics that allow us to record the quality of a given model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.modeling.metrics import mean_absolute_error\n\npredictions = my_model.predict(testing_table)\nmae_score = mean_absolute_error(\n    df=predictions, y_true_col_names="SHIFT_SALES", y_pred_col_names="PRED_SHIFT_SALES"\n)\nmae_score\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E4: Log the Model\u003C/h3\u003E\n","\u003Cp\u003EFinally, we can log this model, and its version, comments, and metrics into the registry that we created above. The final line of this code prints the results, where we see that this model is now deployed to our registry where we can review, improve, and call the model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Log the model\nmodel_ver = reg.log_model(\n    model_name="SHIFT_SALES_MODEL", version_name="Version_1", model=my_model\n)\n\n# Add a description to the model -\nmodel_ver.set_metric(metric_name="MAE Score", value=mae_score)\nmodel_ver.comment = "This linear regression model predicts the Shift Sales for a given Location, using Features discovered through Sigma"\n\nreg.get_model("SHIFT_SALES_MODEL").show_versions()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E5: Give Permissions to Sigma\u003C/h3\u003E\n","\u003Cp\u003EThanks to Sigma’s direct to CDW connection, all we have to do is give the Sigma Role access, and the model will be automatically available to Sigma!\u003C/p\u003E\n","\u003Cp\u003E<aside class="positive">\n<strong>IMPORTANT:</strong><br> Make sure to update this to your registry and your Sigma role.\n</aside>\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession.sql("GRANT USAGE ON ALL MODELS IN SCHEMA SE_DEMO_DB.ML_REGISTRY TO ROLE PAPERCRANE").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EUsing the Model in Sigma\u003C/h2\u003E\n","\u003Cp\u003EWe’ll now show how we can apply that trained model in sigma, and look at an example application of that method.\u003C/p\u003E\n","\u003Cp\u003E1: Create a child table from our \u003Ccode\u003ETest\u003C/code\u003E table, and call it \u003Ccode\u003EDeploy Model.\u003C/code\u003E We’ll be calling our model here.\u003C/p\u003E\n","\u003Cp\u003E2: Create a new column, and use the following syntax and your own model location to define a function like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-code\"\u003ECallVariant(“SE_DEMO_DB.ML_REGISTRY.SHIFT_SALES_MODEL!Predict”) \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see an error about argument types, as we haven’t provided any input yet. If you get an error about the UDF not existing, there is likely a permissions error.\u003C/p\u003E\n","\u003Cp\u003E<aside class="positive">\n<strong>IMPORTANT:</strong><br> Make sure you have given usage to the model as described in Section 5. Visual Studio Code Programming, Step 6.\n</aside>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml25.png\" alt=\"assets/ml25.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E3: Now let’s add the arguments. These should be provided in the same order as in your code:\n\u003Ccode\u003EMONTH_OF_DATE\u003C/code\u003E, \u003Ccode\u003EWEEKDAY_OF_DATE\u003C/code\u003E, \u003Ccode\u003EENCODED_SHIFT\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EVoila, you should now see a JSON output in this column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml26.png\" alt=\"assets/ml26.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E4: Finally, we can now extract the prediction from the column. Sigma \u003Ca href=\"https://help.sigmacomputing.com/docs/json\"\u003Ereads JSON right out of the box\u003C/a\u003E, so we can just right click and extract the columns.\u003C/p\u003E\n","\u003Cp\u003EFor linear regression, there is only one output, \u003Ccode\u003EPRED_SHIFT_SALES\u003C/code\u003E, that we care about, but we could imagine other models that would have multiple outputs here.\u003C/p\u003E\n","\u003Cp\u003EConfirm your selection, and we have our final prediction that directly runs the model we defined in Snowflake:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml27.png\" alt=\"assets/ml27.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml28.png\" alt=\"assets/ml28.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E5: Now, if we combine the steps of the prediction above, we end up with a final syntax that looks something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-code\"\u003ENumber(CallVariant("SE_DEMO_DB.ML_REGISTRY.SHIFT_SALES_MODEL!PREDICT", [Month of Date], [Weekday of Date], [Encoded Shift]).PRED_SHIFT_SALES)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat’s quite a mouthful, and it is clearly unrealistic to expect later business users to invoke the model in this way. Luckily, we can use a \u003Ca href=\"https://help.sigmacomputing.com/docs/custom-functions\"\u003ESigma Custom Function\u003C/a\u003E to encode this for future use in a friendlier syntax.\u003C/p\u003E\n","\u003Cp\u003EOpen up your \u003Ccode\u003EAdmin Panel\u003C/code\u003E to access the custom functions:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml29.png\" alt=\"assets/ml29.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E6: Currently, \u003Ccode\u003ECustom Functions\u003C/code\u003E are located on the bottom of our \u003Ccode\u003EAdmin Panel\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EScroll down to the bottom of the page, and then select \u003Ccode\u003EAdd\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml30.png\" alt=\"assets/ml30.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E7: We can paste the formula from Step 5 into the formula box, and then update to your own model location.\u003C/p\u003E\n","\u003Cp\u003EWe can then give the Custom Function an easy-to-find name, \u003Ccode\u003EPredictShiftSales\u003C/code\u003E - and let our users know what it does:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml31.png\" alt=\"assets/ml31.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E8: Now, we can define the arguments, again using User-Friendly variable names and descriptions. You don’t need the descriptions, but they are a great way to explain and specify what users should enter here.\u003C/p\u003E\n","\u003Cp\u003EFor this QuickStart, you can just use the names \u003Ccode\u003EMonth Number\u003C/code\u003E, \u003Ccode\u003EWeekday Number\u003C/code\u003E and \u003Ccode\u003EShift Number\u003C/code\u003E and save the descriptions for later:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml32.png\" alt=\"assets/ml32.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E9: Once we update the formula bar to use these new friendly names, making sure to maintain the order of the arguments, we can save the Custom Function and it will now be available in our workbook:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml33.png\" alt=\"assets/ml33.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E10: Go back to the \u003Ccode\u003EML Shift Sales\u003C/code\u003E workbook and add a \u003Ccode\u003Enew column\u003C/code\u003E to our \u003Ccode\u003EDeploy Model\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003EYou’ll be able to now run the exact same ML model by entering in \u003Ccode\u003EPredictShiftSales\u003C/code\u003E and filling in the arguments!\u003C/p\u003E\n","\u003Cp\u003EThis simple format for calling the model will make your Model far more accessible to the end users who stand to benefit from it:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml34.png\" alt=\"assets/ml34.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n","\u003Ch2\u003EExtended Applications\u003C/h2\u003E\n","\u003Cp\u003EThis section documents examples of how different personas can benefit from deployed ML functions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1: Business Ops: Scoring new data through Input tables:\u003C/strong\u003E<br>\nIt’s very common for organizations to have operational steps outside the CDW, in the format of Excel or Google Sheets files. Incorporating those files into a Machine Learning framework has historically involved a fair amount of friction. In Sigma, we can do it very simply using an input table. The input table allows us to paste the values from a Google Sheets table, and then transform the variables for the model, and apply the model all in one step:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml35.png\" alt=\"assets/ml35.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOur Business Op persona can then quickly identify the shifts with the most predicted earnings and allocate more resources to those shifts! In this example, we use a RankPercentile function to find the top and bottom 10% predicted shifts, and mark those for Boosting and Dropping, respectively ml36.png\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2: Data Apps: Scoring a specific shift:\u003C/strong\u003E<br>\nSuppose you want to build your manager a tool that allows them to know whether a specific shift is expected to perform well or not. We can build that Data App in seconds in Sigma. In our example, we create two controls for the date and the shift. We can then handle the transformation within a Dynamic Text Element, such that the control is properly formatted for the Model Call. As a result, we get a ready-made Scoring App, where any business user can tweak what day or shift they want to get the prediction for:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml37.png\" alt=\"assets/ml37.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3: Data Science: Reviewing the quality of a model:\u003C/strong\u003E<br>\nSigma can also be an excellent place to check the accuracy and performance of an ML model. In this example, we run the Custom Function against our Test Set and compare the output against the actual observed shift sales we saw for that day. We can create a new column, \u003Ccode\u003EResidual\u003C/code\u003E, that measures the difference between the observed and predicted value.\u003C/p\u003E\n","\u003Cp\u003EThen, the residuals can be plotted to see if the predictive power of our model is sufficient for our use case, or if further refinements in Snowpark or EDA are needed. Because Sigma is so flexible in the calculations and groupings we can apply, customers use Sigma for all sorts of statistical applications, including Power Analyses, Confusion Matrices, and Lift, ROC, and Gain charts:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml38.png\" alt=\"assets/ml38.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF SECTION-->\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a training dataset, trained a model, and exposed it in a easy to use medium through a Sigma front end. This exercise is just scratching the surface of what is possible with Snowflake, Snowpark, and Sigma.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to explore and build a dataset for training a model\u003C/li\u003E\u003Cli\u003EHow to build a machine learning model using Snowpark ML\u003C/li\u003E\u003Cli\u003EHow to register a model in the Snowpark Model Registry\u003C/li\u003E\u003Cli\u003EHow to expose the model to business users in Sigma\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.sigmacomputing.com/blog/\"\u003EBlog\u003C/a\u003E<br>\n\u003Ca href=\"https://community.sigmacomputing.com/\"\u003ECommunity\u003C/a\u003E<br>\n\u003Ca href=\"https://help.sigmacomputing.com/hc/en-us\"\u003EHelp Center\u003C/a\u003E<br>\n\u003Ca href=\"https://quickstarts.sigmacomputing.com/\"\u003EQuickStarts\u003C/a\u003E<br>\u003C/p\u003E\n","\u003Cp\u003EBe sure to check out all the latest developments at \u003Ca href=\"https://quickstarts.sigmacomputing.com/firstfridayfeatures/\"\u003ESigma's First Friday Feature page!\u003C/a\u003E\n<br>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://twitter.com/sigmacomputing\"\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/twitter.png\" alt=\"./assets/twitter.png\"\u003E\u003C/a\u003E \n\u003Ca href=\"https://www.linkedin.com/company/sigmacomputing\"\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/linkedin.png\" alt=\"./assets/linkedin.png\"\u003E\u003C/a\u003E \n\u003Ca href=\"https://www.facebook.com/sigmacomputing\"\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/facebook.png\" alt=\"./assets/facebook.png\"\u003E\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png\" alt=\"Footer\"\u003E\u003C/p\u003E\n<!-- END OF WHAT WE COVERED -->\n<!-- END OF QUICKSTART -->"],"title":"Develop a Predictive Model using Snowflake and Sigma",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"lastUpdated: 2024-07-09\r\n\r\n\u003C!-- The above name is what appears on the website and is searchable. --\u003E\r\n\r\n## Overview \r\n\r\nThis QuickStart will guide you through an end-to-end example of utilizing Snowflake's new machine learning features. \r\n\r\nOur focus will be on creating a machine learning-driven price prediction tool, integrating the perspectives of both a business analyst and a data scientist using Snowflake and Sigma.\r\n\r\nWe will demonstrate how Sigma enables the execution of complex commands in Snowflake, and displays results in a format similar to a spreadsheet interface. \r\n\r\nThis approach not only maintains data security within Snowflake, but also broadens accessibility to users without SQL expertise.\r\n\r\nIn our example, we'll analyze historic food truck sales data. We aim to develop a model identifying key drivers of high sales in the past and explore how a business analyst can leverage this model for informed decision-making. The analyst will be able to collaborate with the data scientist all from a sigma workbook.\r\n\r\n### Target Audience\r\nAnyone who is interested in learning how to easily leverage the power of Snowflakes machine learning features, by using Sigma.\r\n\r\n### What You Will Build\r\nIn this lab you will be creating a machine learning-driven price prediction tool, integrating the perspectives of both a business analyst and a data scientist using Snowflake and Sigma.\r\n\r\n### What You Will Learn\r\n\r\n- How to explore and build a dataset for training a model\r\n- How to build a machine learning model using Snowpark ML\r\n- How to register a model in the Snowpark Model Registry\r\n- How to expose the model to business users in Sigma\r\n\r\n### Prerequisites\r\nThanks for signing up for Snowflake & Sigma’s Hands-On Lab, “Develop a Predictive Model Using Snowflake and Sigma”. To ensure that you can follow along during the lab, please complete the work outlined below prior to the start of the lab. \r\n\r\n1: Sign up for a Snowflake Trial - You can sign up for a 30-day free trial of Snowflake [here.](https://signup.snowflake.com/) Even if you have a login on an existing Snowflake account, you should still create a new Snowflake account, as you’ll be asked to utilize the ACCOUNTADMIN role for some steps below.\r\n\r\n\r\n2: Accept Anaconda Terms - Follow [these instructions](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-setup#label-notebooks-anaconda-terms) to accept the Anaconda Terms to import Python libraries in Snowflake. (For this lab, you will need to import the [snowflake-ml-python](https://pypi.org/project/snowflake-ml-python/) package in a Snowflake Notebook.)\r\n\r\n\r\n3: Sign Up for a Sigma Trial - From Snowflake, in Snowsight, navigate to `Data Product`s \u003E\u003E `Partner Connect`, then search for `Sigma` and follow the instructions to sign up for a free trial. When signing up for a Sigma trial via Partner Connect, relevant Snowflake objects, such as a database (PC_SIGMA_DB), a compute warehouse (PC_SIGMA_WH), a role (PC_SIGMA_ROLE), and a user (PC_SIGMA_USER) for working with Snowflake data in Sigma. Furthermore, Partner Connect will automatically create a Snowflake connection for you in Sigma. (Snowflake PC_SIGMA_WH).\r\n\r\nOnce you are done configuring partner connect, or if you already have a Sigma account (or [created a Sigma trial manually](https://www.sigmacomputing.com/free-trial)), and did not use Partner Connect to create these Snowflake objects, you can run the following commands in a [Snowsight Worksheet](https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs#create-worksheets-in-sf-web-interface) to configure your HOL environment:\r\n\r\n```code\r\nUSE ROLE SYSADMIN;\r\nCREATE DATABASE IF NOT EXISTS PC_SIGMA_DB;\r\nCREATE WAREHOUSE IF NOT EXISTS PC_SIGMA_WH\r\n\r\n    WAREHOUSE_SIZE='XSMALL'\r\n    INITIALLY_SUSPENDED=TRUE\r\n    AUTO_SUSPEND=120;\r\n\r\nUSE ROLE SECURITYADMIN;\r\n\r\nCREATE ROLE IF NOT EXISTS PC_SIGMA_ROLE;\r\n\r\nGRANT ALL ON DATABASE PC_SIGMA_DB TO ROLE PC_SIGMA_ROLE;\r\nGRANT USAGE ON WAREHOUSE PC_SIGMA_WH TO ROLE PC_SIGMA_ROLE;\r\n\r\nGRANT ROLE PC_SIGMA_ROLE TO ROLE SYSADMIN;\r\nGRANT ROLE PC_SIGMA_ROLE TO USER \u003Cyour-snowflake-user\u003E;\r\n\r\n\r\nUSE ROLE SYSADMIN;\r\nCREATE DATABASE IF NOT EXISTS SIGMA_INTERNAL;\r\nCREATE SCHEMA IF NOT EXISTS SIGMA_INTERNAL.WRITEBACK;\r\n\r\n\r\nUSE ROLE SECURITYADMIN;\r\nGRANT ALL ON DATABASE SIGMA_INTERNAL TO ROLE PC_SIGMA_ROLE;\r\nGRANT ALL ON SCHEMA SIGMA_INTERNAL.WRITEBACK TO ROLE PC_SIGMA_ROLE;\r\n```\r\n\r\n4: The following notebook has been provided for you to load into your Snowflake interface.\r\n\r\n\u003Cbutton\u003E[Download Snowflake Notebook](https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/notebooks/notebook_app.ipynb)\u003C/button\u003E\r\n\r\n![assets/pm1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/pm1.png)\r\n\r\nUpon import, Snowflake will prompt you for a database and schema to be used in the notebook. You can use any database/schema that the Sigma Service Role has access to.\r\n\r\n\u003Cul\u003E\r\n  \u003Cli\u003EA Sigma instance that has a connection established to your own Snowflake instance.\u003C/li\u003E\r\n  \u003Cli\u003ESigma Write-back is enabled to your Snowflake environment\u003C/li\u003E\r\n  \u003Cli\u003EA snowflake role with Usage rights on a Snowflake schema that the Sigma Service Role has access to, as well as to the writeback location.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Setup\r\n\r\nWe're starting from scratch with a blank Sigma workbook. I'll first load our sales data from Snowflake. If you have sales data in Snowflake, we can directly connect to it from Sigma. In this case, we don’t have that data in Snowflake, so we’ll need to upload it. Fortunately, that’s easy to do in Sigma. Let's upload a CSV file of shift sales from the city of Seattle.\r\n\r\n\u003Cbutton\u003E[Download required CSV file](https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/SHIFT_SALES.csv)\u003C/button\u003E\r\n\r\n1: Download the CSV file from this [Sigma hosted location:](https://sigma-quickstarts-main.s3.us-west-1.amazonaws.com/csv/SHIFT_SALES.csv)\r\n\r\n\r\n2: From the Sigma home page click the \"Create New\" button in the top left corner and select \"Workbook\". \r\n\r\n![assets/ml1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml1.png)\r\n\r\n3: Now that you are in the Workbook, let's start by saving it with the name \"ML Shift Sales\" by clicking \"Save As\" in the top right. \r\n\r\n![assets/ml3.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml3.png)\r\n\r\n4: Add a new table, and then select Upload CSV as an option  \r\n\r\n![assets/ml4.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml4.png)\r\n\r\n5: Make sure your instance is selected in the connection dropdown (NOT the Sigma Sample Database), and then drag your downloaded file into the upload area and then press save in the upper right to upload the table to your Snowflake instance and view the table in your sigma workbook. \r\n\r\n\r\n![assets/ml5.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml5.png)\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Sigma Data Exploration\r\n\r\n1: Shift sales will be of primary importance for this QuickStart, so let’s adjust adjust its format to `currency` at the start of our analysis. Select the `Shift Sales` column, and use the buttons next to the formula bar to adjust the format:\r\n\r\n![assets/ml6.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml6.png)\r\n\r\n2: We don’t know what variables impact sales, but it’s a safe bet to start with time having some kind of impact. Let’s make a visual to explore this. Create a `Child element` visual: \r\n\r\n![assets/ml7.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml7.png)\r\n\r\nDrag `Date` and `Shift Sales` columns to the `X` and `Y axis`, respectively:\r\n\r\n![assets/ml8.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml8.png)\r\n\r\n\r\n3: We can see that Sigma automatically aggregates our data to the `Day level` and applies a `Sum`. \r\n\r\nLets adjust this to a monthly aggregation to \"quiet out\" some of the noise. \r\n\r\nYou can adjust the formula directly in the formula bar:\r\n\r\n![assets/ml9.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml9.png)\r\n\r\n\r\n4: We can see the seasonality of the sales around each January, and we can isolate this further to confirm that suspicion. \r\n\r\n\r\nWe will switch the formula to a [datepart() function](https://help.sigmacomputing.com/docs/datepart), and see that the first 3 months do indeed have the highest sales:\r\n\r\n![assets/ml10.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml10.png)\r\n\r\n\r\n5: The second factor that we think may play a role in the sales is the shift that the sales took place in. \r\n\r\n\r\nWe can easily add that to our visual, and then switch to a “No Stacking” bar chart, to see the differences between AM and PM shifts:\r\n\r\n![assets/ml11.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml11.png)\r\n\r\n\r\n6: The third factor that we think may play a role is the weekday that the sales took place on. This is a very similar question to our monthly analysis. \r\n\r\n\r\nWe can duplicate the table:\r\n\r\n![assets/ml12.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml12.png)\r\n\r\nDrag it next to our first chart: \r\n \r\n![assets/ml13.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml13.png)\r\n\r\nAnd then adjust the `DatePart() function` to use `weekday`.\r\n\r\nJust like in the months, we can see that certain weekdays definitely return greater sales:\r\n\r\n![assets/ml14.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml14.png)\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Create a Dataset for Modeling\r\n\r\n\r\nNow that we have identified Month Number, Weekday, and Shift as potential predictors for shift sales, let’s prepare a dataset with these variables for our data scientist. In Sigma, this may be something that a data scientist does in the existing workbook, or the analyst can prepare it with guidance. There is lots of room for collaboration through [live edit](https://help.sigmacomputing.com/docs/workbook-collaboration-with-live-edit) and our [comment feature.](https://help.sigmacomputing.com/docs/workbook-comments)\r\n\r\n1: Create a child table of our shift sales table and drag it to the bottom of our workbook:\r\n\r\n![assets/ml15.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml15.png)\r\n\r\n2: For our training set, we want the data to be filtered before a certain date so that we can then assess shifts after that date in the test set. \r\n\r\n\r\nWe right click the `Date` column, and then `filter`:\r\n\r\n![assets/ml16.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml16.png)\r\n\r\nSet the filter to `Between` the dates `1/1/2020 and 12/31/2022`, to get 3 years of training data:\r\n\r\n![assets/ml17.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml17.png)\r\n\r\nThen, rename this table to `Train` by double-clicking the title, to edit it.\r\n\r\n\r\n3: Now we need to create the columns that we found to drive Sales. \r\n\r\n\r\nAdd a column: \r\n\r\n![assets/ml18.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml18.png)\r\n\r\nDefine it as `DatePart(“month”, [Date])`, so that we get the month number.\r\n\r\n\r\n4: We can easily duplicate this column to make a weekday column as well. \r\n\r\n\r\n`Duplicate` the column and then change `month` to `weekday` in the formula:\r\n\r\n![assets/ml19.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml19.png)\r\n\r\n\r\n5: Finally, your data scientist may want you to encode categorical data into numerical values. \r\n\r\nWe can easily do this with Sigma using a formula definition. \r\n\r\n\r\nAdd a new column, define it's function as `If([Shift] = \"AM\", 1, 0)`, and then rename it to `Encoded Shift`.: \r\n\r\n![assets/ml20.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml20.png)\r\n\r\n\r\n6: Now, we need to repeat all the steps to make a Test table....**Just kidding!!**\r\n\r\n\r\nAll we need to do is `duplicate` the table: \r\n\r\n![assets/ml21.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml21.png)\r\n\r\nAdjust the `date filter` so that it gives us values `on or after 1/1/2023`, and then rename the new table to `Test`:\r\n\r\n![assets/ml22.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml22.png)\r\n\r\n7: Finally, we can make all this work available in your Snowflake Write-back schema by creating a warehouse view from the Train table. \r\n\r\nWe recommend calling it `Train`, but you can name it anything you’d like:\r\n\r\n![assets/ml23.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml23.png)\u003Cbr\u003E\r\n\r\n![assets/ml24.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml24.png)\r\n \r\n\u003Caside class=\"negative\"\u003E\r\n\u003Cstrong\u003ENOTE:\u003C/strong\u003E\u003Cbr\u003E Note that this will get us a fully qualified name that our data scientist can use in their programming.\r\n\u003C/aside\u003E\r\n\r\n8: Repeat this for `Test` to create a warehouse view for the Test table.\r\n\r\n9: Publish the workbook to create the warehouse views!\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Snowflake Programming\r\n\r\nWe can now let our data scientist know that TASTY_BITES_TRAIN is ready for them to train their model on Month, Weekday, and Shift. The Data Scientist can now begin their work in the Snowflake Notebook that was downloaded in the beginning section of this hands on lab.\r\n\r\nYou can run those chunks one at a time and read their notations in the notebook, as well as see the general steps outlined below:\r\n\r\n### 1: Create a Model Registry\r\n\r\nThis is created in your database that you will use to store this and future models.\r\n\r\n```python\r\nfrom snowflake.ml.registry import Registry\r\n\r\nsession.sql(\"CREATE SCHEMA IF NOT EXISTS ML_REGISTRY\").collect()\r\n\r\nreg = Registry(session, database_name=\"SE_DEMO_DB\", schema_name=\"ML_REGISTRY\")\r\n```\r\n\r\n### 2: Snowpark ML Functions\r\n\r\nThe new Snowpark ML functions make it super easy to train open-source models on optimized and scalable Snowflake compute. We can set up a code block that allows us to easily train a decision tree regression model with just a few lines. You will use the warehouse view locations that you generated in your sigma workbook in the session table calls below:\r\n\r\n```python\r\nfrom snowflake.ml.modeling.tree import DecisionTreeRegressor\r\n\r\n# Specify inputs\r\ntraining_table = session.table(\"[USE WAREHOUSE VIEW LOCATION FOR TRAIN]\")\r\ntesting_table = session.table(\"[USE WAREHOUSE VIEW LOCATION FOR TEST]\")\r\n\r\n# Input my Analyst's ideas for features\r\nfeature_cols = [\r\n    \"MONTH_OF_DATE\",\r\n    \"WEEKDAY_OF_DATE\",\r\n    \"ENCODED_SHIFT\",\r\n]\r\ntarget_col = \"SHIFT_SALES\"\r\n\r\nmy_model = DecisionTreeRegressor()\r\nmy_model.set_input_cols(feature_cols)\r\nmy_model.set_label_cols(target_col)\r\nmy_model.set_output_cols(\"PRED_\" + target_col)\r\n\r\nmy_model.fit(training_table)\r\n```\r\n\r\n### 3: Create Governed Metrics\r\n\r\nSnowflake also offers a large library of metrics that allow us to record the quality of a given model.\r\n\r\n```python\r\nfrom snowflake.ml.modeling.metrics import mean_absolute_error\r\n\r\npredictions = my_model.predict(testing_table)\r\nmae_score = mean_absolute_error(\r\n    df=predictions, y_true_col_names=\"SHIFT_SALES\", y_pred_col_names=\"PRED_SHIFT_SALES\"\r\n)\r\nmae_score\r\n```\r\n\r\n\r\n### 4: Log the Model\r\n\r\nFinally, we can log this model, and its version, comments, and metrics into the registry that we created above. The final line of this code prints the results, where we see that this model is now deployed to our registry where we can review, improve, and call the model.\r\n\r\n```python\r\n# Log the model\r\nmodel_ver = reg.log_model(\r\n    model_name=\"SHIFT_SALES_MODEL\", version_name=\"Version_1\", model=my_model\r\n)\r\n\r\n# Add a description to the model -\r\nmodel_ver.set_metric(metric_name=\"MAE Score\", value=mae_score)\r\nmodel_ver.comment = \"This linear regression model predicts the Shift Sales for a given Location, using Features discovered through Sigma\"\r\n\r\nreg.get_model(\"SHIFT_SALES_MODEL\").show_versions()\r\n```\r\n\r\n### 5: Give Permissions to Sigma\r\n\r\nThanks to Sigma’s direct to CDW connection, all we have to do is give the Sigma Role access, and the model will be automatically available to Sigma! \r\n\r\n\u003Caside class=\"positive\"\u003E\r\n\u003Cstrong\u003EIMPORTANT:\u003C/strong\u003E\u003Cbr\u003E Make sure to update this to your registry and your Sigma role.\r\n\u003C/aside\u003E\r\n\r\n```python\r\nsession.sql(\"GRANT USAGE ON ALL MODELS IN SCHEMA SE_DEMO_DB.ML_REGISTRY TO ROLE PAPERCRANE\").collect()\r\n```\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Using the Model in Sigma\r\n\r\nWe’ll now show how we can apply that trained model in sigma, and look at an example application of that method. \r\n\r\n1: Create a child table from our `Test` table, and call it `Deploy Model.` We’ll be calling our model here. \r\n\r\n2: Create a new column, and use the following syntax and your own model location to define a function like this:\r\n```code \r\nCallVariant(“SE_DEMO_DB.ML_REGISTRY.SHIFT_SALES_MODEL!Predict”) \r\n```\r\n\r\nYou should see an error about argument types, as we haven’t provided any input yet. If you get an error about the UDF not existing, there is likely a permissions error. \r\n\r\n\u003Caside class=\"positive\"\u003E\r\n\u003Cstrong\u003EIMPORTANT:\u003C/strong\u003E\u003Cbr\u003E Make sure you have given usage to the model as described in Section 5. Visual Studio Code Programming, Step 6.\r\n\u003C/aside\u003E\r\n\r\n![assets/ml25.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml25.png)\r\n\r\n3: Now let’s add the arguments. These should be provided in the same order as in your code: \r\n`MONTH_OF_DATE`, `WEEKDAY_OF_DATE`, `ENCODED_SHIFT`. \r\n\r\nVoila, you should now see a JSON output in this column. \r\n\r\n![assets/ml26.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml26.png)\r\n\r\n4: Finally, we can now extract the prediction from the column. Sigma [reads JSON right out of the box](https://help.sigmacomputing.com/docs/json), so we can just right click and extract the columns. \r\n\r\nFor linear regression, there is only one output, `PRED_SHIFT_SALES`, that we care about, but we could imagine other models that would have multiple outputs here. \r\n\r\nConfirm your selection, and we have our final prediction that directly runs the model we defined in Snowflake:\r\n\r\n![assets/ml27.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml27.png)\r\n\r\n\u003Cbr\u003E\r\n\r\n![assets/ml28.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml28.png)\r\n\r\n5: Now, if we combine the steps of the prediction above, we end up with a final syntax that looks something like this:\r\n```code\r\nNumber(CallVariant(\"SE_DEMO_DB.ML_REGISTRY.SHIFT_SALES_MODEL!PREDICT\", [Month of Date], [Weekday of Date], [Encoded Shift]).PRED_SHIFT_SALES)\r\n```\r\n\r\nThat’s quite a mouthful, and it is clearly unrealistic to expect later business users to invoke the model in this way. Luckily, we can use a [Sigma Custom Function](https://help.sigmacomputing.com/docs/custom-functions) to encode this for future use in a friendlier syntax. \r\n\r\nOpen up your `Admin Panel` to access the custom functions:\r\n\r\n![assets/ml29.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml29.png)\r\n\r\n6: Currently, `Custom Functions` are located on the bottom of our `Admin Panel`. \r\n\r\nScroll down to the bottom of the page, and then select `Add`:\r\n\r\n![assets/ml30.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml30.png)\r\n\r\n7: We can paste the formula from Step 5 into the formula box, and then update to your own model location. \r\n\r\nWe can then give the Custom Function an easy-to-find name, `PredictShiftSales` - and let our users know what it does:\r\n\r\n![assets/ml31.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml31.png)\r\n\r\n8: Now, we can define the arguments, again using User-Friendly variable names and descriptions. You don’t need the descriptions, but they are a great way to explain and specify what users should enter here. \r\n\r\nFor this QuickStart, you can just use the names `Month Number`, `Weekday Number` and `Shift Number` and save the descriptions for later:\r\n\r\n![assets/ml32.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml32.png)\r\n\r\n9: Once we update the formula bar to use these new friendly names, making sure to maintain the order of the arguments, we can save the Custom Function and it will now be available in our workbook:\r\n\r\n![assets/ml33.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml33.png)\r\n\r\n10: Go back to the `ML Shift Sales` workbook and add a `new column` to our `Deploy Model` table. \r\n\r\nYou’ll be able to now run the exact same ML model by entering in `PredictShiftSales` and filling in the arguments! \r\n\r\nThis simple format for calling the model will make your Model far more accessible to the end users who stand to benefit from it:\r\n\r\n![assets/ml34.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml34.png)\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n## Extended Applications\r\n\r\nThis section documents examples of how different personas can benefit from deployed ML functions.\r\n\r\n\r\n**1: Business Ops: Scoring new data through Input tables:**\u003Cbr\u003E\r\nIt’s very common for organizations to have operational steps outside the CDW, in the format of Excel or Google Sheets files. Incorporating those files into a Machine Learning framework has historically involved a fair amount of friction. In Sigma, we can do it very simply using an input table. The input table allows us to paste the values from a Google Sheets table, and then transform the variables for the model, and apply the model all in one step:\r\n\r\n![assets/ml35.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml35.png)\r\n\r\nOur Business Op persona can then quickly identify the shifts with the most predicted earnings and allocate more resources to those shifts! In this example, we use a RankPercentile function to find the top and bottom 10% predicted shifts, and mark those for Boosting and Dropping, respectively ml36.png\r\n\r\n**2: Data Apps: Scoring a specific shift:**\u003Cbr\u003E\r\nSuppose you want to build your manager a tool that allows them to know whether a specific shift is expected to perform well or not. We can build that Data App in seconds in Sigma. In our example, we create two controls for the date and the shift. We can then handle the transformation within a Dynamic Text Element, such that the control is properly formatted for the Model Call. As a result, we get a ready-made Scoring App, where any business user can tweak what day or shift they want to get the prediction for:\r\n\r\n![assets/ml37.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml37.png)\r\n\r\n**3: Data Science: Reviewing the quality of a model:**\u003Cbr\u003E\r\nSigma can also be an excellent place to check the accuracy and performance of an ML model. In this example, we run the Custom Function against our Test Set and compare the output against the actual observed shift sales we saw for that day. We can create a new column, `Residual`, that measures the difference between the observed and predicted value.\r\n\r\nThen, the residuals can be plotted to see if the predictive power of our model is sufficient for our use case, or if further refinements in Snowpark or EDA are needed. Because Sigma is so flexible in the calculations and groupings we can apply, customers use Sigma for all sorts of statistical applications, including Power Analyses, Confusion Matrices, and Lift, ROC, and Gain charts:\r\n\r\n![assets/ml38.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/ml38.png)\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\u003C!-- END OF SECTION--\u003E\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion And Resources\r\n\r\n\r\nCongratulations! You've successfully built a training dataset, trained a model, and exposed it in a easy to use medium through a Sigma front end. This exercise is just scratching the surface of what is possible with Snowflake, Snowpark, and Sigma.\r\n\r\n\r\n### What You Learned\r\n\r\n- How to explore and build a dataset for training a model\r\n- How to build a machine learning model using Snowpark ML\r\n- How to register a model in the Snowpark Model Registry\r\n- How to expose the model to business users in Sigma\r\n\r\n### Related Resources\r\n\r\n[Blog](https://www.sigmacomputing.com/blog/)\u003Cbr\u003E\r\n[Community](https://community.sigmacomputing.com/)\u003Cbr\u003E\r\n[Help Center](https://help.sigmacomputing.com/hc/en-us)\u003Cbr\u003E\r\n[QuickStarts](https://quickstarts.sigmacomputing.com/)\u003Cbr\u003E\r\n\r\nBe sure to check out all the latest developments at [Sigma's First Friday Feature page!](https://quickstarts.sigmacomputing.com/firstfridayfeatures/)\r\n\u003Cbr\u003E\r\n\r\n[![./assets/twitter.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/twitter.png)](https://twitter.com/sigmacomputing) \r\n[![./assets/linkedin.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/linkedin.png)](https://www.linkedin.com/company/sigmacomputing) \r\n[![./assets/facebook.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/facebook.png)](https://www.facebook.com/sigmacomputing)\r\n\r\n![Footer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/partner-snowflake-predictive-model-using-sigma/sigma_footer.png)\r\n\r\n\u003C!-- END OF WHAT WE COVERED --\u003E\r\n\u003C!-- END OF QUICKSTART --\u003E",":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-ad5499dec3","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-206d83bbfb",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-5fa917a19e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-25",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-dd2a7a9bc2","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. It may be out of date with current Snowflake 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