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Overview\u003C/h2\u003E\n","\u003Cp\u003EThis guide will take you through the process of performing supervised machine learning by building a binary classification model to help predict whether a customer is likely to churn (that is, no longer be our customer).\u003C/p\u003E\n","\u003Cp\u003ESupervised machine learning is the process of taking a historical dataset with KNOWN outcomes of what we would like to predict, to train a model, that can be used to make future predictions.\u003C/p\u003E\n","\u003Cp\u003EThe historical dataset we will be working with today is taken from a fictitious telecommunications company, and contains details about a customers plan usage, spend patterns, issues logged with customer support, geographical information, and a binary value (hence binary classification) indicating whether that person ended their contract with the company (churned). Here is a snippet of the data:\n<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p1.png\" alt=\"\"\u003E\n<br/><br/>\nThroughout this process, you will discover the ease at which it is possible to build and deploy machine learning models by leveraging the scale and performance of Snowflake and the automation of the DataRobot platform.\u003C/p\u003E\n","\u003Cp\u003ETo showcase the full lifecycle of a deploying machine learning model, we will first load in Snowflake, our data cloud where all of our data lives. Then, we will leverage the DataRobot auto-pilot process to perform exploratory data analysis, and then train, evaluate, and interpret a slew of potential machine learning models. And finally, use the Snowflake-DataRobot prediction integration to score our model against new data, as if the model was in production.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic knowledge of SQL, and database concepts and objects\u003C/li\u003E\u003Cli\u003EBasic understanding of data science and machine learning\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create and utilize databases, tables, views, and warehouses\u003C/li\u003E\u003Cli\u003ELoad structured data into Snowflake\u003C/li\u003E\u003Cli\u003EUse Snowflake’s “Partner Connect” to seamlessly create a DataRobot trial\u003C/li\u003E\u003Cli\u003EAutomatically create, run, and evaluate prediction models in DataRobot\u003C/li\u003E\u003Cli\u003EWrite results to Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\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 (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\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake objects, including tables and warehouses, to scale and manage your data\u003C/li\u003E\u003Cli\u003EOver 50+ machine learning models automatically to measure their performance and interpret\u003C/li\u003E\u003Cli\u003EA prediction pipeline to automate scoring new data on your deployed model\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://raw.githubusercontent.com/Snowflake-Labs/sfquickstarts/refs/heads/master/site/sfguides/src/automl-with-snowflake-and-datarobot/Snowflake_Datarobot_VHOL_guides.sql\"\u003EDownload .sql File\u003C/a\u003E\n</button>\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EAt this point, log into your Snowflake account and open a new \u003Ccode\u003EWorksheet\u003C/code\u003E. If you have just created a free trial account, you will land in the \u003Ccode\u003ELearn\u003C/code\u003E section. Simply navaigate to the \u003Ccode\u003EWorksheets\u003C/code\u003E tab on the left and click \u003Ccode\u003E+ Worksheet\u003C/code\u003E in the top right hand corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p51.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ETo ingest our script in the Snowflake UI, click the down arrow next to the time your notebook was created in the top left hand side of your screen and load our \u003Ccode\u003ESnowflake_Datarobot_VHOL_guies.sql\u003C/code\u003E script using the \u003Ccode\u003EImport SQL from File\u003C/code\u003E button. You can also change the name of this worksheet to "Snowflake-DataRobot VHOL Summit 2022"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p53.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides "worksheets" as the spot for you to execute your code. For each worksheet you create, you will need to set the “context” so the worksheet knows how to behave. A “context” in Snowflake is made up of 4 distinctions that must be set before we can perform any work: the “role” we want to act as, the “database” and “schema” we want to work with, and the “warehouse” we want to perform the work.\u003C/p\u003E\n","\u003Cp\u003ELets go ahead and set the role we want to act as, which will be \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E to begin with. This can either be done manually in the UI or programmatically in a worksheet (\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E is the default role for a first time user). Lets do so programmatically in our worksheet by executing our first line of code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo execute this code, all we need to do is place our cursor on the line we wish to run and then either hit the "run" button at the top left of the worksheet or press \u003Ccode\u003ECmd/Ctrl + Enter\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn addition to traditional SQL statements, Snowflake Data Definition (\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql-ddl-summary.html\"\u003EDDL\u003C/a\u003E) commands, such as setting the worksheet context, can also be written and executed within the worksheet.\u003C/p\u003E\n","\u003Cp\u003EEach step throughout the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code line by line as we walk through the lab.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a Snowflake Database\u003C/h2\u003E\n","\u003Cp\u003EDuration 10:\u003C/p\u003E\n","\u003Cp\u003ETo get started, we are going to create our first Snowflake object and \u003Ccode\u003ECREATE\u003C/code\u003E a database called \u003Ccode\u003ECUSTOMER_DATA\u003C/code\u003E that will be used for loading the structured data for our current example. To get back to our main navigation menu, simply click on the \u003Ccode\u003EHOME\u003C/code\u003E button at the top left hand side of the worksheet.\u003C/p\u003E\n","\u003Cp\u003EFrom here, click on the "Data" tab and click \u003Ccode\u003E+ Database\u003C/code\u003E, to create a new database object. Every Snowflake trial provides you with two default databases, which is why you already see database objects on your list.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p54.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EEnter \u003Ccode\u003ECUSTOMER_DATA\u003C/code\u003E in the "Name" section, and click "Create".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p55.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou should now see your new databases add to the list. Lets navigate back to our \u003Ccode\u003ESnowflake-DataRobot VHOL Summit 2022\u003C/code\u003E worksheet. You should see our worksheet with all of the SQL we loaded in the prior step. In the future you can skip this step by executing the following line of code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE customer_data;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will continue to set the context for our worksheet. The "role": \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, we already set earlier. To set our "database", "schema", and "warehouse", execute the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE WAREHOUSE compute_wh WITH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\n\nUSE DATABASE customer_data;\nUSE SCHEMA public;\nUSE WAREHOUSE compute_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOne thing to note is, we already have already executed some SQL commands without a warehouse attached. So why did they work then? Because the only commands executed so far are DDL commands. These commands are free in Snowflake. Only when you start working with data does a warehouse turn on.\u003C/p\u003E\n","\u003Cp\u003EContinuing, trial accounts automatically come with \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E created for you. If you already had an account that you are using for this lab, you probably didn't have this warehouse anymore. Whether you did or you didn't have this warehouse, the \u003Ccode\u003EOR REPLACE\u003C/code\u003E part of the \u003Ccode\u003ECREATE OR REPLACE WAREHOUSE compute_wh\u003C/code\u003E command will simply replace the old warehouse object if it already existed.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a Snowflake Table\u003C/h2\u003E\n","\u003Cp\u003EAs part of the lab, we are going to create 2 tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ETRAIN_DATA\u003C/code\u003E -- The dataset that we will use to train our machine learning model\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESCORING_DATA\u003C/code\u003E -- Out of sample data that we will use to score and validate our model\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe will be using our worksheet exclusively to create the table. You should see the following block of code in your worksheet. Execute the code by highlighting or putting your cursor on the following block of code and press \u003Ccode\u003ECmd/Ctrl + Enter\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE train_data (\n    "CUST_ID" NUMBER(38,0),\n    "CHURN" BOOLEAN,\n    "STATE" VARCHAR(16777216),\n    "ACCOUNT_LENGTH" NUMBER(38,0),\n    "AREA_CODE" NUMBER(38,0),\n    "INTERNATIONAL" VARCHAR(16777216),\n    "VOICEMAIL_PLAN" VARCHAR(16777216),\n    "NUM_VM_MESSAGES" NUMBER(38,0),\n    "TOTAL_DAY_MINS" FLOAT,\n    "TOTAL_DAY_CALLS" NUMBER(38,0),\n    "TOTAL_DAY_CHARGE" FLOAT,\n    "TOTAL_EVE_MINS" FLOAT,\n    "TOTAL_EVE_CALLS" NUMBER(38,0),\n    "TOTAL_EVE_CHARGE" FLOAT,\n    "TOTAL_NIGHT_MINS" FLOAT,\n    "TOTAL_NIGHT_CALLS" NUMBER(38,0),\n    "TOTAL_NIGHT_CHARGE" FLOAT,\n    "TOTAL_INT_MINS" FLOAT,\n    "TOTAL_INT_CALLS" NUMBER(38,0),\n    "TOTAL_INT_CHARGE" FLOAT,\n    "CUSTOMER_SERVICE_CALLS" NUMBER(38,0),\n    "TARRIF_PLAN_CONDS" VARCHAR(16777216)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify that your table \u003Ccode\u003ETRAIN_DATA\u003C/code\u003E has been created. At the bottom of the worksheet you should see a “Results” section which says “Table TRAIN_DATA successfully created.”\u003C/p\u003E\n","\u003Cp\u003ENow let’s do the same to create the \u003Ccode\u003ESCORING_DATA\u003C/code\u003E table. Run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE scoring_data (\n    "CUST_ID" NUMBER(38,0),\n    "CHURN" BOOLEAN,\n    "STATE" VARCHAR(16777216),\n    "ACCOUNT_LENGTH" NUMBER(38,0),\n    "AREA_CODE" NUMBER(38,0),\n    "INTERNATIONAL" VARCHAR(16777216),\n    "VOICEMAIL_PLAN" VARCHAR(16777216),\n    "NUM_VM_MESSAGES" NUMBER(38,0),\n    "TOTAL_DAY_MINS" FLOAT,\n    "TOTAL_DAY_CALLS" NUMBER(38,0),\n    "TOTAL_DAY_CHARGE" FLOAT,\n    "TOTAL_EVE_MINS" FLOAT,\n    "TOTAL_EVE_CALLS" NUMBER(38,0),\n    "TOTAL_EVE_CHARGE" FLOAT,\n    "TOTAL_NIGHT_MINS" FLOAT,\n    "TOTAL_NIGHT_CALLS" NUMBER(38,0),\n    "TOTAL_NIGHT_CHARGE" FLOAT,\n    "TOTAL_INT_MINS" FLOAT,\n    "TOTAL_INT_CALLS" NUMBER(38,0),\n    "TOTAL_INT_CHARGE" FLOAT,\n    "CUSTOMER_SERVICE_CALLS" NUMBER(38,0),\n    "TARRIF_PLAN_CONDS" VARCHAR(16777216)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAt the top of the worksheet, go click on the "home" tab, click on the "data" tab, click on the  \u003Ccode\u003ECUSTOMER_DATA\u003C/code\u003E database, click on the \u003Ccode\u003EPUBLIC\u003C/code\u003E schema, and click on "Tables". You should see your newly created \u003Ccode\u003ETRAIN_DATA\u003C/code\u003E and \u003Ccode\u003ESCORING_DATA\u003C/code\u003E tables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p57.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou can go one level deeper here and look at the column definitions for each table by clicking on the table name and then "Columns".\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a Snowflake External Stage\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage.html\"\u003EStages\u003C/a\u003E in snowflake are places that you can land your data before it is uploaded to a Snowflake table. You might have a batch of CSV files living on a disk driver somewhere, and, in order to start querying the data via a table, the data must be landed within the Snowflake environment for a data upload to be possible.\u003C/p\u003E\n","\u003Cp\u003EIn the exercise, we will be working with structured, comma-delimited data that has already been staged in a public, external AWS bucket. Before we can use this data, we first need to create a \u003Ccode\u003EStage\u003C/code\u003E that specifies the location of our external bucket.\u003C/p\u003E\n","\u003Cp\u003ELet’s create the "stage" object. "Stages" are typically created by Storage Administrators, but for the purposes of this lab, YOU will be creating this object. Again, lets click back on "Worksheets" tab  on the left hand side and find our way back to our \u003Ccode\u003ESnowflake-DataRobot VHOL Summit 2022\u003C/code\u003E worksheet, and excute the next following lines of code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE sf_dr_stage\n  URL = 's3://snowflake-workshop-lab/telecoms';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe also can take a look at the contents of the \u003Ccode\u003Esf_dr_stage\u003C/code\u003E by executing the follow:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Els @sf_dr_stage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the output in the “Results” window in the bottom pane:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p58.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHere is the two csv files we will be using for our example project today.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a Snowflake File Format\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-file-format.html\"\u003EFile Formats\u003C/a\u003E tell Snowflake the structure of the data coming in. The last thing that we need to do before we can load the data into our Snowflake tables is: we have to create a \u003Ccode\u003EFile Format\u003C/code\u003E that matches the data structure of the local files we want to upload. As smart as Snowflake is, its not THAT smart.\u003C/p\u003E\n","\u003Cp\u003EFor our example, our data has header columns in the CSV, so we want to skip those. A comma delimiter is the default way to delimit CSV files (hence the name), but sometimes you can choose another character. We need to give Snowflake all the details on how we have organized our data in the files we want to load in. Please execute the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FILE FORMAT churndata_ff\n  TYPE = 'CSV'\n  COMPRESSION = 'AUTO'\n  FIELD_DELIMITER = ','\n  RECORD_DELIMITER = '\\n'\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = 'NONE'\n  TRIM_SPACE = FALSE\n  ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE\n  ESCAPE = 'NONE'\n  ESCAPE_UNENCLOSED_FIELD = '\\134'\n  DATE_FORMAT = 'AUTO'\n  TIMESTAMP_FORMAT = 'AUTO'\n  NULL_IF = ('\\\\N');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p59.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoading Data into Snowflake\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we will use a virtual \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-overview.html\"\u003Ewarehouse\u003C/a\u003E and the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html\"\u003ECOPY command\u003C/a\u003E to initiate bulk loading of the CSV file sitting in our AWS external stage, moving it into the Snowflake table we just created.\u003C/p\u003E\n","\u003Cp\u003ESnowflake Warehouses are where the innovation of cloud computing comes to life, allowing scale across both workload dimensions. For situations in which your data size has outgrown your infrastructure, Snowflake allows you to scale up (literally make bigger) the warehouse size to meet the computational demands of your query. As the demand for your data grows, Snowflake allows you to scale out (literally add more compute workers) the number of servers to meet the concurrency demands of your increasing influx of users. This is all billed at a per-second level granularity granting you the ability to truly only pay for what you use.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p17.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ECommon workloads are data loading, running a query, or performing a Data Manipulation Language (\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql-dml.html#general-dml\"\u003EDML\u003C/a\u003E) operation. Each workload can have its own virtual warehouse so it doesn’t compete for resource with other workloads.\u003C/p\u003E\n","\u003Cp\u003EIn section 3, when we were setting up our context, we used the default warehouse \u003Ccode\u003ECOMPUTE_WH(XS)\u003C/code\u003E that came with a trial account (or created it if we were using a different account)\u003C/p\u003E\n","\u003Cp\u003ELets go ahead and instantaneously scale up the size of our warehouse to better match the size of our data. We can go from an \u003Ccode\u003EXSMALL\u003C/code\u003E warehouse and double our available compute to a \u003Ccode\u003ESMALL\u003C/code\u003E warehouse by running the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER WAREHOUSE compute_wh SET\n  WAREHOUSE_SIZE = "SMALL";\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEarly when we created our warehouse, we gave it some additional parameters to consider. \u003Ccode\u003EAUTO_SUSPEND = 60\u003C/code\u003E tells the warehouse that after a query has finished running, stay alive for exactly 1 more minute in case new queries are going to come and take advantage Snowflake's data caching abilities. 60 seconds is the MIN and can be adjusted upwards. \u003Ccode\u003EAUTO_RESUME = TRUE\u003C/code\u003E tells the warehouse to turn back on once the user begins submitting queries to the warehouse. And lastly, \u003Ccode\u003EINITIALLY_SUSPENDED = TRUE\u003C/code\u003E tells the warehouse to initially not turn on when the warehouse is completed. What these parameters enable is a true pay for what you consume billing pattern. With that, we can be assured that even if we walk away from the computer, we have peace of mind we aren't accruing idle compute costs.\u003C/p\u003E\n","\u003Cp\u003ENow we can run a COPY command to load the data into the \u003Ccode\u003ECUSTOMER_DATA\u003C/code\u003E table we created earlier. Go ahead and execute the next set of statements in the worksheet to load the staged data into the table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECOPY INTO train_data FROM @sf_dr_stage/Churn_Telecomms_training.csv\n  FILE FORMAT = (FORMAT_NAME = churndata_ff);\n\nCOPY INTO scoring_data FROM @sf_dr_stage/Churn_Telecomms_scoring.csv\n  FILE FORMAT = (FORMAT_NAME = churndata_ff);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the Results window, you should see the status of the load:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p60.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe now finally have data inside a Snowflake table that is ready to be queried on demand. We can see a sample of what data lies within our table by execute the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM train_data LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see something like this. Note we have our \u003Ccode\u003EHURN\u003C/code\u003E column. This will be the key column that we will go and build a supervised machine learning model on.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p61.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnecting Snowflake with DataRobot\u003C/h2\u003E\n","\u003Cp\u003EAt this point in time, we have our data sitting in an optimized table within Snowflake that is available for a variety of different downstream functions. Snowflake does not offer machine learning capabilities, and therefore, happily partners with the leading data science and machine learning partners in the industry. We are on a mission to help us figure out which of our customers are most likely to churn and DataRobot can help us build a machine learning model to answer that question.\u003C/p\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. To get here, click our "Home" button and then navigate to "Admin" and then "Partner Connect". 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. Click the "Data Science & ML" category and click "DataRobot".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p62.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe have all the Snowflake objects we need created already, so press "Connect".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p65.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGetting Started with DataRobot\u003C/h2\u003E\n","\u003Cp\u003EGo to your email, and verify your account, you will be redirected to the DataRobot account registration page.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p43.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDataRobot is software-as-a-service (SaaS) application deployed on the cloud for your convenience, allowing you to build machine learning models without having to provision and manage any compute infrastructure. Go ahead and enter your name and password, and then click "Sign Up" for us to proceed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p25.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe next screen will ask us a few more questions to tailor our onboarding experience. For the purposes of this lab, we are going to stick with the theme that we are a data scientist working in the telecom industry, but feel free to choose the options that you identify with best. Go ahead and choose "Data Scientist" as our role, and "Telecom" as our industry. Then hit "Next."\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p26.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe are interested in developing models, so lets go ahead and choose "Create AI Models." Then hit "Start."\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p27.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe will now land on the DataRobot main home page. Feel free to take a peak around. You can always get back to this screen by clicking on the "DataRobot" icon at the top left of the screen. On the top right of the screen, you will see a bunch of orange icons. On the very right, you should see the "people" icon. It will take us to a bunch of options regarding our account.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a DataRobot Data Connection\u003C/h2\u003E\n","\u003Cp\u003ETo start a new ML project - on the top right click on the button '+ Create new project' \u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p28.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ETo import the data for the ML project click the button 'Data Source' - We will be creating the connection that maps to the context we were using when in the Snowflake platform.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p29.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ENext we can go ahead and click "Add new data connection" on the near top left hand side of the screen. Right above where you see your pre-configured data connection.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p33.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou'll then be asked to choose what type of connection you want to create. DataRobot supports connections to many types of databases. Lets go ahead and choose Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p34.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe now will have a bunch of fields to fill in. The first is "Data connection name." I just called mine \u003Ccode\u003ESnowflake HOL\u003C/code\u003E given this is a "Snowflake DataRobot Hands On Lab." For the driver dropdown, go ahead and choose the recommanded one Next, paste the url we just copied into the "address" box (without the 'http://'). Our "db" will be \u003Ccode\u003ECUSTOMER_DATA\u003C/code\u003E and the "warehouse" is \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E. Finally, lets go ahead and add two more parameters. By clicking the "Add parameter box," let search for the "role" parameter and set it to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E and then "schema" and set it to \u003Ccode\u003EPUBLIC\u003C/code\u003E. Click "Add data connection"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p35.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EFrom here you will now see your new data connection. We now need to associate our Snowflake login to this connection. If we didnt, anyone with the deployment URL could use the connection string!\u003C/p\u003E\n","\u003Cp\u003EChoose 'Basic' and enter your credentials - user name and password and click 'Save and sign in'\u003C/p\u003E\n","\u003Cp\u003EOnce the connection succeeded click on 'Add new data source'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p36.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EOnce the connection succeeded click on 'Add new data source'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p37.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EOn the Search by: choose Tables and type 'train' - you will see the table 'TRAIN_DATA', select this table and click 'Create project'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p38.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting up a DataRobot Project\u003C/h2\u003E\n","\u003Cp\u003EWhen we want to take some data and leave with a model to deploy, the culmination of all these steps are stored in a DataRobot "project." A new project is kicked off as soon as you load your data into the system, just like we did. When your data is upload, DataRobot then begins performing exploratory data analysis (EDA), the first step in a typical machine learning lifecycle. This is a combination of detecting the data types and showing the number of unique, missing, mean, median, standard deviation, and minimum and maximum values. This information is helpful for getting a sense of the data shape and distribution.\u003C/p\u003E\n","\u003Cp\u003EIf we scroll down we can see that automated data quality check:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr24.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EAnd underneath, we can see all of the columns that were present in our raw dataset.\u003C/p\u003E\n","\u003Cp\u003EClick on the feature called \u003Ccode\u003ETOTAL_DAY_MINS\u003C/code\u003E and you will see the following histogram:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr1.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EHere we see user counts by bucket based on how long they talk on the phone during the day. The reason a plot like this is useful is to spot outliers, data that is far away from the main distribution. This can clue you in to whether your dataset is clean of any possible data collection errors.\u003C/p\u003E\n","\u003Cp\u003ENext, we can scroll back up and select a target. This is the feature that answers the question: what do you want to predict. In our case, is did customer stay or leave (CHURN), this is the feature you want the model to predict. You can type in the name of the prediction target or click "U​se as target​ next" to the name in the feature list. Upon this selection, DataRobot will analyze your target variable and automatically determine the type of machine learning problem we will be performing today (in this case, binary classification). DataRobot will also choose what optimization it thinks is best based off the distribution of our target variable.\u003C/p\u003E\n","\u003Cp\u003EOnce you select the name of the target, you should see the following screen:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr2.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EIf you want to customize the model building process, you can modify a variety of advanced parameters, optimization metrics, feature lists, transformations, partitioning, and sampling options. The default modeling mode is “Quick”. For more control over which algorithms DataRobot runs, there are manual, Autopilot and comprehensive options.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStarting DataRobot Quick Autopilot\u003C/h2\u003E\n","\u003Cp\u003ELets get to building models automatically. Go ahead and click the “Start​” button to kick off DataRobots Autopilot process. DataRobot will continue to compute a variety of different statistics to help aid our machine learning problem. One of these steps is computing a feature's "Alternating Conditional Expectation." This is in essence an “Importance” grade that  tells you how much a given feature helps predict what you are looking to predict in an isolated fashion.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr3.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou can again drill down on features in our feature list to view distributions, but this time, with the target variable overlaid on the image. In this case, the greater the number of customer service calls, the higher the probability of that customer churning, which intuitively makes sense.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr5.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EMoving over to the "Models" tab, DataRobot supports popular open source software frameworks, including software available in programming languages like Python and R and libraries such as XGBoost and more. During the automated modeling process, DataRobot analyzes the characteristics of the training data and the selected prediction target, and selects the most appropriate machine learning algorithms to apply, also known as a DataRobot "Blueprint". DataRobot blueprints is a collection of preprocessing steps plus the algorithm, and includes operations like one-hot encoding, missing value imputation, text mining, etc.\u003C/p\u003E\n","\u003Cp\u003EDataRobot streamlines model development, in a leaderboard type fashion, by automatically ranking models (or ensembles of models) based on the techniques advanced data scientists use, including boosting, bagging, random forests, kernel-based methods, generalized linear models, deep learning, and many others. By cost-effectively evaluating a near-infinite combination of data transformations, features, algorithms, and tuning parameters in parallel across a large cluster of servers, DataRobot delivers the best predictive model in the shortest amount of time.\u003C/p\u003E\n","\u003Cp\u003EAnd of course, recommends a model for deployment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr4.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ELet's take a 7 min break, and when we come back, we will analyze the results.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEvaluating the "Recommended For Deployment" Model\u003C/h2\u003E\n","\u003Cp\u003ELet's 'star' the first model in the leadeboard. This is the model that was the most optimal given our chosen optimization metric. Let's star it. To start evaluating it, we can click on the model which will present use with the following options: Evaluate, Understand, Describe, and Predict (​additional tabs may be present based on extra features that are enabled).\u003C/p\u003E\n","\u003Cp\u003EBefore we deep dive on each tab let's go to the Understand tab > Feature Effects and click 'Compute Feature Effects'\u003C/p\u003E\n","\u003Cp\u003EDataRobot offers superior transparency, interpretability, and explainability to help you better understand how models were built and give you the confidence to explain to others why a model made the predictions it did. If we go back and click the “D​escribe”​ tab, you can view the end-to-end model blueprint containing details of the specific feature engineering tasks and algorithms DataRobot used to run the model. In addition to an automated compliance report (In non trial accounts)\u003C/p\u003E\n","\u003Cp\u003EIn non trial accounts this flow can be customized - Composable ML provides a full-flexibility approach to model building, allowing you to direct your data science and subject matter expertise to the models you build. With Composable ML, you build blueprints that best suit your needs using built-in tasks and custom Python/R code.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr9.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EClick on "Evaluate". The “Evaluate” option includes: Lift Chart, ROC Curve (for classification models), Confusion Matrix, Feature Fit, and Advanced Tuning.\u003C/p\u003E\n","\u003Cp\u003EThe Lift Chart depicts how well a model segments the target population and how capable it is of predicting the target, letting you visualize the model's effectiveness. Looking at the Lift Chart, the left side of the curve indicates where the model predicted a low score on one section of the population while the right side of the curve indicates where the model predicted a high score. In general, the steeper the actual line is, and the more closely the predicted line matches the actual line, the better the model is. A consistently increasing line is another good indicator.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr7.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe ROC Curve tab helps to explore classification, performance, and statistics related to the selected model at any point on the probability scale. What that means is, at what probability of someone leaving do we raise an alarm? 30% chance? 50% chance? By play with different scenarios, we let get to see the outcomes of different scenarios. This tab contains a set of interactive graphical displays that include an ROC curve, a prediction distribution, cumulative charts, and a confusion matrix. There is also a display of summary statistics that describe model performance at the selected threshold. Each of these components combine to provide an interactive snapshot of the behavior of a model's classification power.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr8.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EIn the “U​nderstand”​ tab, popular exploratory capabilities include Feature Impact, Feature Effects, Prediction Explanations, and Word Cloud (depending on the features in the dataset). These all help enlighten you on what drives a model’s predictions.\u003C/p\u003E\n","\u003Cp\u003EDataRobot uses permutation importance to estimate feature impact. “Feature Impact” measures how much each feature contributes to the overall accuracy of the model. For this example, we can see that the most impactful reason a person is likely to stay as a customer or leave is the number of \u003Ccode\u003ECUSTOMER_SERVICE_CALLS\u003C/code\u003E. The real question is, now how do we go reduce that number :) so that less people leave. This is just another way to take information from the machine learning lifecycle to add value to a business.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr10.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe “Feature Effects” chart displays model details on a per-feature basis (a feature's effect on the overall prediction). This gives you insight into understanding the relationship between each variable and the target. The insight is communicated in terms of partial dependence, which illustrates how a change in a feature's value, while keeping all other features as they were, impacts a model's predictions. Here we can see that once the number of \u003Ccode\u003ECUSTOMER_SERVICE_CALLS\u003C/code\u003E goes above 3, the likelihood of the customer leaving rapidly increases.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr11.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003E“Prediction Explanations” reveal the reasons why DataRobot generated a particular prediction for a certain data point. This gives you can ammunition to back up decisions with specific details. They provide a quantitative indicator of a variable's effect on an individual prediction.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr12.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ELastly, DataRobot’s "Insights" tab at the top provides more graphical representations of your model. There are tree-based variable rankings, hotspots, variable effects to illustrate the magnitude and direction of a feature's effect on a model's predictions, text mining charts, anomaly detection, and a word cloud of keyword relevancy.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr14.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploying our model and using Job Defintions with Snowflake\u003C/h2\u003E\n","\u003Cp\u003EEvery model built in DataRobot is immediately ready for deployment. And there a several methods of scoring new data against this Deployment\u003C/p\u003E\n","\u003Cp\u003EYou can: upload a new dataset to be scored in batch, create a REST API endpoint to score data directly from applications, export the model for in-place database scoring, or use a partner integration. Today we will use the Job Definitions feature.\u003C/p\u003E\n","\u003Cp\u003EIf you click the number in the "Prediction Threshold" box, you can see a few different easy options to set your threshold. Again, at what probability do we say, "Yes this person is going to churn." Choose "Maximize F1". Then click on the “Deploy Model” button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr15.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EHere we are presented with a bunch of options to tweak our deployment.\nThe model can be deploy to any prediction server - even deploy to Snowflake\u003C/p\u003E\n","\u003Cp\u003EScroll down to the main section "Association ID." Here we want to type in \u003Ccode\u003ECUST_ID\u003C/code\u003E. This is going to allow use to track predictions by customer when we go to monitor our model.\u003C/p\u003E\n","\u003Cp\u003EThen go ahead and toggle the "Require association ID in prediction requests" and the 4 other option all under "Data Drift.""\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr16.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EScroll back up. We will see all of our boxes are green except for 'Continuous AI'\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr17.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ETo maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. (we won't test it today)\u003C/p\u003E\n","\u003Cp\u003EClick "Create Deployment"\u003C/p\u003E\n","\u003Cp\u003EThis will present us with a screen to give a priority setting to this deployment. We can skip and click "Create deployment." This may take a couple of minutes to create the deployment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr18.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EAfter creating the new deployment - click on 'return to deployments'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p39.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe Deployments Inventory shows the real-time status of all deployed models, regardless of how they were created or when and where they were deployed.. Here you can manage, monitor, and govern your deployed models and seeing at a glance which models are stale, preventing risk to your business.\u003C/p\u003E\n","\u003Cp\u003EAnd now we have one active deployment. Go ahead and click on it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr19.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe have different capabilities as part of MLOps, including service health, data drift, accuracy, Embedded Governance, Humility, Fairness and more, that we won't cover today.\u003C/p\u003E\n","\u003Cp\u003EGo to the "Predictions" tab then "Job Definitions" tab and click on "+Add job definition".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr20.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe will start setting up our job definition.\u003C/p\u003E\n","\u003Cp\u003Eclick on '+ Define connection' next to the Prediction source and choose the 'Snowflake HOL' connection and your user.\nOn the Search by: choose Tables and type 'scoring' - you will see the table 'SCORING_DATA', select this table and click 'Save connection'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr21.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe "Prediction Source" is now complete. You can adjust the prediction options if needed, for this walkthrough we're going to stick with the defaults.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr22.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe last section to complete is the "Prediction Destination". Go ahead and leave the Destination Type as "JDBC", and click "+ Define Connection".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr23.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ERepeat the process as before in the "Prediction Source" (choose the data connection) and on tables create a new table\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr26.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EGo ahead and click “Create a table”\nSchema: 'CUSTOMER_DATA.PUBLIC'\nname: SCORED_DATA' and click 'Save connection'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr27.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EChange the write strategy to 'Insert'.\u003C/p\u003E\n","\u003Cp\u003EAt the bottom you can schedule this job to run on a Schedule, or just run it manually. Go ahead and click "Save Prediction job definition" in the bottom left, then click on "View all Job Definitions" in the upper left. Click the hamburger icon on the right side of the job definition you just made, and click "Run now".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr29.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EIf you click the hamburger icon again and go to “View job history” you can see its status. Once it's finished you should see the green “Succeeded” message under status.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr30.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EWe can now finally head back to our Snowflake tab. If you hit the refresh icon near the top left of our screen by your databases, you should see the \u003Ccode\u003ESCORED_DATA\u003C/code\u003E table that was created once we kicked off our prediction job. If you excecute the query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    *\nFROM\n    customer_data.public.scored_data;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see our data with three new columns: 'CHURN_True_Prediction', 'CHURN_False_Prediction' showing the likelihood that each person is likely to churn or stay, and the decision in the column 'CHURN_PREDICTION'\u003C/p\u003E\n","\u003Cp\u003EClick on the 'CHURN_PREDICTION'column, on the right side, we can see that snowflake snowsight presents statistics - In this case, looks like 430 (which is 17.5%) of the customers for this dataset are likely to churn. It also offers additional charts for further analysis.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr25.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EAnd as they say, that is all folks!\u003C/p\u003E\n","\u003Cp\u003EFor the advanced python users you can keep the lab to see how to use python for snowpark togetehr with DataRobot.\u003C/p\u003E\n","\u003Ch2\u003E(Extra) Connecting Snowflake with Zepl - DataRobot notebooks in the cloud\u003C/h2\u003E\n","\u003Cp\u003EGo back to the partner connect section in Snowflake and click the "Data Science & ML" category and click "Zepl"\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p62.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThis will present you with a screen to connect to Zepl. 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 click "Connect" .\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p63.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou then will be prompted to activate your account now, click the blue "Activate" button to do so.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p64.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will launch a new tab to the Zepl platform.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGetting Started with Zepl - DataRobot notebooks in the cloud\u003C/h2\u003E\n","\u003Cp\u003EDataRobot Zepl the cloud data science notebook solution that enables advanced data scientists to do exploratory, code-centric work in Python, R, and Scala with enterprise features such as collaboration, versioning, and security.\u003C/p\u003E\n","\u003Cp\u003EIn this exercise, we will use snowpark python and datarobot zepl notebook to do some EDA and visualizatoion\u003C/p\u003E\n","\u003Cp\u003EFirst, let's configure Zepl:\nAfter creating your zepl account through the partner connect download the following notebook:\u003C/p\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://app.zepl.com/viewer/notebooks/bm90ZTovL2F0YWxpYS5ob3JlbnNodGllbkBkYXRhcm9ib3QuY29tLzAxYjY3NjdiMDk0ZDQwZjE5YmI1Njg1MjJlODI2MDJjL25vdGUuanNvbg\"\u003EDownload the notebook\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EClick on my My Notebooks\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p66.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EImport the notebook: click import on the right side, upload the notebook that was preshared and once the notebook appears in your screen click on the notebook name: Snowflake_HOL\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating a Zepl Data Connection\u003C/h2\u003E\n","\u003Cp\u003ETo create a data connection from a notebook do the following:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the data sources sidebar by clicking the data sources icon on the right side of the notebook\u003C/li\u003E\u003Cli\u003EClick the Add new button at the top of the sidebar and select 'Secret store'\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p42.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EFill in the fields:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003Ea. NAME: 'Snowflake_DR_HOL'\u003C/p\u003E\n","\u003Cp\u003Eb. KEY VALUES:\u003C/p\u003E\n","\u003Cp\u003EKey: 'password'\u003C/p\u003E\n","\u003Cp\u003EValue: your snowflake account password\u003C/p\u003E\n","\u003Cp\u003EAnd click on the button 'Add'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p41.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EYou will see the new secret under the 'Attached to this notebook'\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p67.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect to Snowflake Snowpark\u003C/h2\u003E\n","\u003Cp\u003EDownload the Snowpark library:\n<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://drive.google.com/file/d/15kVt0XtGIKl7CrN27jpUHwcW4MvdIdiA/view?usp=sharin\"\u003EDownload the Snowpark library\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003ETo import the Snowpark library:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the folder sidebar by clicking the folder icon on the right side of the notebook\u003C/li\u003E\u003Cli\u003EImport the Snowpark library that was preshared\u003C/li\u003E\u003Cli\u003EWhen you see the file, hover on the file and click the symbol '</>' and copy the path\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p69.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EModify Section1:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E-Replace the path in line1 with the copied path\u003C/p\u003E\n","\u003Cp\u003E-Change the id number in line2 - the line id is teh id from the path\u003C/p\u003E\n","\u003Cp\u003ENow you can run in the notebook Sections 1-2:\u003C/p\u003E\n","\u003Cp\u003ESection 1 will download and install the snowpark python package\u003C/p\u003E\n","\u003Cp\u003ESection 2 will import the necessery packages\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EModify Section3:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003Eaccount: change to your Snowflake account name (WITHOUT snowflakecomputing.com)\u003C/p\u003E\n","\u003Cp\u003Euser: change to your user name\u003C/p\u003E\n","\u003Cp\u003ENow you can run Sections 3-4:\u003C/p\u003E\n","\u003Cp\u003ESection 3 will connect to snowflake where we created the training table\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p68.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003ESection 4 will present the data in the training table we created in snowflake, query is running in Snowflake\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p70.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEDA on Zepl notebook using python for snowpark\u003C/h2\u003E\n","\u003Cp\u003EIn the notebook, run Section 5\nThis section will create a new feature to identify if a user is more active at night or day.\nThat can contribute to our model, yo understand things like - why this user churn, when to send this use promotions, etc.\nusing python for snowpark, using the snowflake compute.\nThe results will be overwrtie the train table in Snowflake\u003C/p\u003E\n","\u003Cp\u003EGo back to your snowflake account and run again the sql:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    *\nFROM\n    train_data\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p71.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n<!-- ------------------------ -->"],"description":"Build ML models with DataRobot AutoML on Snowflake for automated feature engineering, model selection, and production deployment.","title":"Accelerating Machine Learning with Snowflake and DataRobot","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Use Case Overview\n\nThis guide will take you through the process of performing supervised machine learning by building a binary classification model to help predict whether a customer is likely to churn (that is, no longer be our customer).\n\nSupervised machine learning is the process of taking a historical dataset with KNOWN outcomes of what we would like to predict, to train a model, that can be used to make future predictions.\n\nThe historical dataset we will be working with today is taken from a fictitious telecommunications company, and contains details about a customers plan usage, spend patterns, issues logged with customer support, geographical information, and a binary value (hence binary classification) indicating whether that person ended their contract with the company (churned). Here is a snippet of the data:\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p1.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\nThroughout this process, you will discover the ease at which it is possible to build and deploy machine learning models by leveraging the scale and performance of Snowflake and the automation of the DataRobot platform.\n\nTo showcase the full lifecycle of a deploying machine learning model, we will first load in Snowflake, our data cloud where all of our data lives. Then, we will leverage the DataRobot auto-pilot process to perform exploratory data analysis, and then train, evaluate, and interpret a slew of potential machine learning models. And finally, use the Snowflake-DataRobot prediction integration to score our model against new data, as if the model was in production.\n### Prerequisites\n- Basic knowledge of SQL, and database concepts and objects\n- Basic understanding of data science and machine learning\n\n### What You'll Learn\n- How to create and utilize databases, tables, views, and warehouses\n- Load structured data into Snowflake\n- Use Snowflake’s “Partner Connect” to seamlessly create a DataRobot trial\n- Automatically create, run, and evaluate prediction models in DataRobot\n- Write results to Snowflake\n\n### What You’ll Need\n- A [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) Account (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\n### What You’ll Build\n- Snowflake objects, including tables and warehouses, to scale and manage your data\n- Over 50+ machine learning models automatically to measure their performance and interpret\n- A prediction pipeline to automate scoring new data on your deployed model\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  \n  [Download .sql File](https://raw.githubusercontent.com/Snowflake-Labs/sfquickstarts/refs/heads/master/site/sfguides/src/automl-with-snowflake-and-datarobot/Snowflake_Datarobot_VHOL_guides.sql)\n\u003C/button\u003E\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nAt this point, log into your Snowflake account and open a new `Worksheet`. If you have just created a free trial account, you will land in the `Learn` section. Simply navaigate to the `Worksheets` tab on the left and click `+ Worksheet` in the top right hand corner.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p51.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nTo ingest our script in the Snowflake UI, click the down arrow next to the time your notebook was created in the top left hand side of your screen and load our `Snowflake_Datarobot_VHOL_guies.sql` script using the `Import SQL from File` button. You can also change the name of this worksheet to \"Snowflake-DataRobot VHOL Summit 2022\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p53.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nSnowflake provides \"worksheets\" as the spot for you to execute your code. For each worksheet you create, you will need to set the “context” so the worksheet knows how to behave. A “context” in Snowflake is made up of 4 distinctions that must be set before we can perform any work: the “role” we want to act as, the “database” and “schema” we want to work with, and the “warehouse” we want to perform the work.\n\nLets go ahead and set the role we want to act as, which will be `ACCOUNTADMIN` to begin with. This can either be done manually in the UI or programmatically in a worksheet (`ACCOUNTADMIN` is the default role for a first time user). Lets do so programmatically in our worksheet by executing our first line of code:\n\n```sql\nUSE ROLE accountadmin;\n```\n\nTo execute this code, all we need to do is place our cursor on the line we wish to run and then either hit the \"run\" button at the top left of the worksheet or press `Cmd/Ctrl + Enter`.\n\n In addition to traditional SQL statements, Snowflake Data Definition ([DDL](https://docs.snowflake.com/en/sql-reference/sql-ddl-summary.html)) commands, such as setting the worksheet context, can also be written and executed within the worksheet.\n\nEach step throughout the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code line by line as we walk through the lab.\n\n\u003C!-- ------------------------ --\u003E\n## Creating a Snowflake Database\nDuration 10:\n\nTo get started, we are going to create our first Snowflake object and `CREATE` a database called `CUSTOMER_DATA` that will be used for loading the structured data for our current example. To get back to our main navigation menu, simply click on the `HOME` button at the top left hand side of the worksheet.\n\nFrom here, click on the \"Data\" tab and click `+ Database`, to create a new database object. Every Snowflake trial provides you with two default databases, which is why you already see database objects on your list.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p54.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nEnter `CUSTOMER_DATA` in the \"Name\" section, and click \"Create\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p55.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou should now see your new databases add to the list. Lets navigate back to our `Snowflake-DataRobot VHOL Summit 2022` worksheet. You should see our worksheet with all of the SQL we loaded in the prior step. In the future you can skip this step by executing the following line of code:\n\n```sql\nCREATE OR REPLACE DATABASE customer_data;\n```\n\nWe will continue to set the context for our worksheet. The \"role\": `ACCOUNTADMIN`, we already set earlier. To set our \"database\", \"schema\", and \"warehouse\", execute the following code:\n\n```sql\nCREATE OR REPLACE WAREHOUSE compute_wh WITH\n  WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\n\nUSE DATABASE customer_data;\nUSE SCHEMA public;\nUSE WAREHOUSE compute_wh;\n```\n\nOne thing to note is, we already have already executed some SQL commands without a warehouse attached. So why did they work then? Because the only commands executed so far are DDL commands. These commands are free in Snowflake. Only when you start working with data does a warehouse turn on.\n\nContinuing, trial accounts automatically come with `COMPUTE_WH` created for you. If you already had an account that you are using for this lab, you probably didn't have this warehouse anymore. Whether you did or you didn't have this warehouse, the `OR REPLACE` part of the `CREATE OR REPLACE WAREHOUSE compute_wh` command will simply replace the old warehouse object if it already existed.\n\n\u003C!-- ------------------------ --\u003E\n## Creating a Snowflake Table\n\nAs part of the lab, we are going to create 2 tables:\n - `TRAIN_DATA` -- The dataset that we will use to train our machine learning model\n - `SCORING_DATA` -- Out of sample data that we will use to score and validate our model\n\nWe will be using our worksheet exclusively to create the table. You should see the following block of code in your worksheet. Execute the code by highlighting or putting your cursor on the following block of code and press `Cmd/Ctrl + Enter`.\n\n```sql\nCREATE OR REPLACE TABLE train_data (\n    \"CUST_ID\" NUMBER(38,0),\n    \"CHURN\" BOOLEAN,\n    \"STATE\" VARCHAR(16777216),\n    \"ACCOUNT_LENGTH\" NUMBER(38,0),\n    \"AREA_CODE\" NUMBER(38,0),\n    \"INTERNATIONAL\" VARCHAR(16777216),\n    \"VOICEMAIL_PLAN\" VARCHAR(16777216),\n    \"NUM_VM_MESSAGES\" NUMBER(38,0),\n    \"TOTAL_DAY_MINS\" FLOAT,\n    \"TOTAL_DAY_CALLS\" NUMBER(38,0),\n    \"TOTAL_DAY_CHARGE\" FLOAT,\n    \"TOTAL_EVE_MINS\" FLOAT,\n    \"TOTAL_EVE_CALLS\" NUMBER(38,0),\n    \"TOTAL_EVE_CHARGE\" FLOAT,\n    \"TOTAL_NIGHT_MINS\" FLOAT,\n    \"TOTAL_NIGHT_CALLS\" NUMBER(38,0),\n    \"TOTAL_NIGHT_CHARGE\" FLOAT,\n    \"TOTAL_INT_MINS\" FLOAT,\n    \"TOTAL_INT_CALLS\" NUMBER(38,0),\n    \"TOTAL_INT_CHARGE\" FLOAT,\n    \"CUSTOMER_SERVICE_CALLS\" NUMBER(38,0),\n    \"TARRIF_PLAN_CONDS\" VARCHAR(16777216)\n);\n```\n\nVerify that your table `TRAIN_DATA` has been created. At the bottom of the worksheet you should see a “Results” section which says “Table TRAIN_DATA successfully created.”\n\nNow let’s do the same to create the `SCORING_DATA` table. Run the following:\n\n```sql\nCREATE OR REPLACE TABLE scoring_data (\n    \"CUST_ID\" NUMBER(38,0),\n    \"CHURN\" BOOLEAN,\n    \"STATE\" VARCHAR(16777216),\n    \"ACCOUNT_LENGTH\" NUMBER(38,0),\n    \"AREA_CODE\" NUMBER(38,0),\n    \"INTERNATIONAL\" VARCHAR(16777216),\n    \"VOICEMAIL_PLAN\" VARCHAR(16777216),\n    \"NUM_VM_MESSAGES\" NUMBER(38,0),\n    \"TOTAL_DAY_MINS\" FLOAT,\n    \"TOTAL_DAY_CALLS\" NUMBER(38,0),\n    \"TOTAL_DAY_CHARGE\" FLOAT,\n    \"TOTAL_EVE_MINS\" FLOAT,\n    \"TOTAL_EVE_CALLS\" NUMBER(38,0),\n    \"TOTAL_EVE_CHARGE\" FLOAT,\n    \"TOTAL_NIGHT_MINS\" FLOAT,\n    \"TOTAL_NIGHT_CALLS\" NUMBER(38,0),\n    \"TOTAL_NIGHT_CHARGE\" FLOAT,\n    \"TOTAL_INT_MINS\" FLOAT,\n    \"TOTAL_INT_CALLS\" NUMBER(38,0),\n    \"TOTAL_INT_CHARGE\" FLOAT,\n    \"CUSTOMER_SERVICE_CALLS\" NUMBER(38,0),\n    \"TARRIF_PLAN_CONDS\" VARCHAR(16777216)\n);\n```\nAt the top of the worksheet, go click on the \"home\" tab, click on the \"data\" tab, click on the  `CUSTOMER_DATA` database, click on the `PUBLIC` schema, and click on \"Tables\". You should see your newly created `TRAIN_DATA` and `SCORING_DATA` tables.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p57.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou can go one level deeper here and look at the column definitions for each table by clicking on the table name and then \"Columns\".\n\n\u003C!-- ------------------------ --\u003E\n## Creating a Snowflake External Stage\n\n[Stages](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage.html) in snowflake are places that you can land your data before it is uploaded to a Snowflake table. You might have a batch of CSV files living on a disk driver somewhere, and, in order to start querying the data via a table, the data must be landed within the Snowflake environment for a data upload to be possible.\n\nIn the exercise, we will be working with structured, comma-delimited data that has already been staged in a public, external AWS bucket. Before we can use this data, we first need to create a `Stage` that specifies the location of our external bucket.\n\nLet’s create the \"stage\" object. \"Stages\" are typically created by Storage Administrators, but for the purposes of this lab, YOU will be creating this object. Again, lets click back on \"Worksheets\" tab  on the left hand side and find our way back to our `Snowflake-DataRobot VHOL Summit 2022` worksheet, and excute the next following lines of code:\n\n```sql\nCREATE OR REPLACE STAGE sf_dr_stage\n  URL = 's3://snowflake-workshop-lab/telecoms';\n```\n\nWe also can take a look at the contents of the `sf_dr_stage` by executing the follow:\n\n```bash\nls @sf_dr_stage;\n```\n\nYou should see the output in the “Results” window in the bottom pane:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p58.png)\n\nHere is the two csv files we will be using for our example project today.\n\n\u003C!-- ------------------------ --\u003E\n## Creating a Snowflake File Format\n\n[File Formats](https://docs.snowflake.com/en/sql-reference/sql/create-file-format.html) tell Snowflake the structure of the data coming in. The last thing that we need to do before we can load the data into our Snowflake tables is: we have to create a `File Format` that matches the data structure of the local files we want to upload. As smart as Snowflake is, its not THAT smart.\n\nFor our example, our data has header columns in the CSV, so we want to skip those. A comma delimiter is the default way to delimit CSV files (hence the name), but sometimes you can choose another character. We need to give Snowflake all the details on how we have organized our data in the files we want to load in. Please execute the following code:\n\n```sql\nCREATE OR REPLACE FILE FORMAT churndata_ff\n  TYPE = 'CSV'\n  COMPRESSION = 'AUTO'\n  FIELD_DELIMITER = ','\n  RECORD_DELIMITER = '\\n'\n  SKIP_HEADER = 1\n  FIELD_OPTIONALLY_ENCLOSED_BY = 'NONE'\n  TRIM_SPACE = FALSE\n  ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE\n  ESCAPE = 'NONE'\n  ESCAPE_UNENCLOSED_FIELD = '\\134'\n  DATE_FORMAT = 'AUTO'\n  TIMESTAMP_FORMAT = 'AUTO'\n  NULL_IF = ('\\\\N');\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p59.png)\n\n\u003C!-- ------------------------ --\u003E\n## Loading Data into Snowflake\n\nIn this section, we will use a virtual [warehouse](https://docs.snowflake.com/en/user-guide/warehouses-overview.html) and the [COPY command](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html) to initiate bulk loading of the CSV file sitting in our AWS external stage, moving it into the Snowflake table we just created.\n\nSnowflake Warehouses are where the innovation of cloud computing comes to life, allowing scale across both workload dimensions. For situations in which your data size has outgrown your infrastructure, Snowflake allows you to scale up (literally make bigger) the warehouse size to meet the computational demands of your query. As the demand for your data grows, Snowflake allows you to scale out (literally add more compute workers) the number of servers to meet the concurrency demands of your increasing influx of users. This is all billed at a per-second level granularity granting you the ability to truly only pay for what you use.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p17.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nCommon workloads are data loading, running a query, or performing a Data Manipulation Language ([DML](https://docs.snowflake.com/en/sql-reference/sql-dml.html#general-dml)) operation. Each workload can have its own virtual warehouse so it doesn’t compete for resource with other workloads.\n\nIn section 3, when we were setting up our context, we used the default warehouse `COMPUTE_WH(XS)` that came with a trial account (or created it if we were using a different account)\n\nLets go ahead and instantaneously scale up the size of our warehouse to better match the size of our data. We can go from an `XSMALL` warehouse and double our available compute to a `SMALL` warehouse by running the following command:\n\n```sql\nALTER WAREHOUSE compute_wh SET\n  WAREHOUSE_SIZE = \"SMALL\";\n```\n\nEarly when we created our warehouse, we gave it some additional parameters to consider. `AUTO_SUSPEND = 60` tells the warehouse that after a query has finished running, stay alive for exactly 1 more minute in case new queries are going to come and take advantage Snowflake's data caching abilities. 60 seconds is the MIN and can be adjusted upwards. `AUTO_RESUME = TRUE` tells the warehouse to turn back on once the user begins submitting queries to the warehouse. And lastly, `INITIALLY_SUSPENDED = TRUE` tells the warehouse to initially not turn on when the warehouse is completed. What these parameters enable is a true pay for what you consume billing pattern. With that, we can be assured that even if we walk away from the computer, we have peace of mind we aren't accruing idle compute costs.\n\nNow we can run a COPY command to load the data into the `CUSTOMER_DATA` table we created earlier. Go ahead and execute the next set of statements in the worksheet to load the staged data into the table:\n\n```sql\nCOPY INTO train_data FROM @sf_dr_stage/Churn_Telecomms_training.csv\n  FILE FORMAT = (FORMAT_NAME = churndata_ff);\n\nCOPY INTO scoring_data FROM @sf_dr_stage/Churn_Telecomms_scoring.csv\n  FILE FORMAT = (FORMAT_NAME = churndata_ff);\n```\n\nIn the Results window, you should see the status of the load:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p60.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe now finally have data inside a Snowflake table that is ready to be queried on demand. We can see a sample of what data lies within our table by execute the following:\n\n```sql\nSELECT * FROM train_data LIMIT 10;\n```\n\nYou should see something like this. Note we have our `HURN` column. This will be the key column that we will go and build a supervised machine learning model on.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p61.png)\n\n\u003C!-- ------------------------ --\u003E\n## Connecting Snowflake with DataRobot\n\nAt this point in time, we have our data sitting in an optimized table within Snowflake that is available for a variety of different downstream functions. Snowflake does not offer machine learning capabilities, and therefore, happily partners with the leading data science and machine learning partners in the industry. We are on a mission to help us figure out which of our customers are most likely to churn and DataRobot can help us build a machine learning model to answer that question.\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. To get here, click our \"Home\" button and then navigate to \"Admin\" and then \"Partner Connect\". 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. Click the \"Data Science & ML\" category and click \"DataRobot\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p62.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe have all the Snowflake objects we need created already, so press \"Connect\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p65.png)\n\n\u003C!-- ------------------------ --\u003E\n## Getting Started with DataRobot\n\nGo to your email, and verify your account, you will be redirected to the DataRobot account registration page.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p43.png)\n\nDataRobot is software-as-a-service (SaaS) application deployed on the cloud for your convenience, allowing you to build machine learning models without having to provision and manage any compute infrastructure. Go ahead and enter your name and password, and then click \"Sign Up\" for us to proceed.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p25.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe next screen will ask us a few more questions to tailor our onboarding experience. For the purposes of this lab, we are going to stick with the theme that we are a data scientist working in the telecom industry, but feel free to choose the options that you identify with best. Go ahead and choose \"Data Scientist\" as our role, and \"Telecom\" as our industry. Then hit \"Next.\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p26.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe are interested in developing models, so lets go ahead and choose \"Create AI Models.\" Then hit \"Start.\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p27.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe will now land on the DataRobot main home page. Feel free to take a peak around. You can always get back to this screen by clicking on the \"DataRobot\" icon at the top left of the screen. On the top right of the screen, you will see a bunch of orange icons. On the very right, you should see the \"people\" icon. It will take us to a bunch of options regarding our account.\n\n\u003C!-- ------------------------ --\u003E\n## Creating a DataRobot Data Connection\n\nTo start a new ML project - on the top right click on the button '+ Create new project' \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p28.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nTo import the data for the ML project click the button 'Data Source' - We will be creating the connection that maps to the context we were using when in the Snowflake platform.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p29.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nNext we can go ahead and click \"Add new data connection\" on the near top left hand side of the screen. Right above where you see your pre-configured data connection.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p33.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou'll then be asked to choose what type of connection you want to create. DataRobot supports connections to many types of databases. Lets go ahead and choose Snowflake.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p34.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe now will have a bunch of fields to fill in. The first is \"Data connection name.\" I just called mine `Snowflake HOL` given this is a \"Snowflake DataRobot Hands On Lab.\" For the driver dropdown, go ahead and choose the recommanded one Next, paste the url we just copied into the \"address\" box (without the 'http://'). Our \"db\" will be `CUSTOMER_DATA` and the \"warehouse\" is `COMPUTE_WH`. Finally, lets go ahead and add two more parameters. By clicking the \"Add parameter box,\" let search for the \"role\" parameter and set it to `ACCOUNTADMIN` and then \"schema\" and set it to `PUBLIC`. Click \"Add data connection\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p35.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nFrom here you will now see your new data connection. We now need to associate our Snowflake login to this connection. If we didnt, anyone with the deployment URL could use the connection string!\n\nChoose 'Basic' and enter your credentials - user name and password and click 'Save and sign in'\n\nOnce the connection succeeded click on 'Add new data source'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p36.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nOnce the connection succeeded click on 'Add new data source'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p37.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nOn the Search by: choose Tables and type 'train' - you will see the table 'TRAIN_DATA', select this table and click 'Create project'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p38.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\u003C!-- ------------------------ --\u003E\n## Setting up a DataRobot Project\n\nWhen we want to take some data and leave with a model to deploy, the culmination of all these steps are stored in a DataRobot \"project.\" A new project is kicked off as soon as you load your data into the system, just like we did. When your data is upload, DataRobot then begins performing exploratory data analysis (EDA), the first step in a typical machine learning lifecycle. This is a combination of detecting the data types and showing the number of unique, missing, mean, median, standard deviation, and minimum and maximum values. This information is helpful for getting a sense of the data shape and distribution.\n\n\nIf we scroll down we can see that automated data quality check:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr24.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nAnd underneath, we can see all of the columns that were present in our raw dataset.\n\nClick on the feature called `TOTAL_DAY_MINS` and you will see the following histogram:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr1.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nHere we see user counts by bucket based on how long they talk on the phone during the day. The reason a plot like this is useful is to spot outliers, data that is far away from the main distribution. This can clue you in to whether your dataset is clean of any possible data collection errors.\n\nNext, we can scroll back up and select a target. This is the feature that answers the question: what do you want to predict. In our case, is did customer stay or leave (CHURN), this is the feature you want the model to predict. You can type in the name of the prediction target or click \"U​se as target​ next\" to the name in the feature list. Upon this selection, DataRobot will analyze your target variable and automatically determine the type of machine learning problem we will be performing today (in this case, binary classification). DataRobot will also choose what optimization it thinks is best based off the distribution of our target variable.\n\nOnce you select the name of the target, you should see the following screen:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr2.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nIf you want to customize the model building process, you can modify a variety of advanced parameters, optimization metrics, feature lists, transformations, partitioning, and sampling options. The default modeling mode is “Quick”. For more control over which algorithms DataRobot runs, there are manual, Autopilot and comprehensive options.\n\n\n\u003C!-- ------------------------ --\u003E\n## Starting DataRobot Quick Autopilot\n\nLets get to building models automatically. Go ahead and click the “Start​” button to kick off DataRobots Autopilot process. DataRobot will continue to compute a variety of different statistics to help aid our machine learning problem. One of these steps is computing a feature's \"Alternating Conditional Expectation.\" This is in essence an “Importance” grade that  tells you how much a given feature helps predict what you are looking to predict in an isolated fashion.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr3.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou can again drill down on features in our feature list to view distributions, but this time, with the target variable overlaid on the image. In this case, the greater the number of customer service calls, the higher the probability of that customer churning, which intuitively makes sense.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr5.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\n\nMoving over to the \"Models\" tab, DataRobot supports popular open source software frameworks, including software available in programming languages like Python and R and libraries such as XGBoost and more. During the automated modeling process, DataRobot analyzes the characteristics of the training data and the selected prediction target, and selects the most appropriate machine learning algorithms to apply, also known as a DataRobot \"Blueprint\". DataRobot blueprints is a collection of preprocessing steps plus the algorithm, and includes operations like one-hot encoding, missing value imputation, text mining, etc.\n\nDataRobot streamlines model development, in a leaderboard type fashion, by automatically ranking models (or ensembles of models) based on the techniques advanced data scientists use, including boosting, bagging, random forests, kernel-based methods, generalized linear models, deep learning, and many others. By cost-effectively evaluating a near-infinite combination of data transformations, features, algorithms, and tuning parameters in parallel across a large cluster of servers, DataRobot delivers the best predictive model in the shortest amount of time.\n\nAnd of course, recommends a model for deployment.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr4.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nLet's take a 7 min break, and when we come back, we will analyze the results.\n\u003C!-- ------------------------ --\u003E\n## Evaluating the \"Recommended For Deployment\" Model\n\nLet's 'star' the first model in the leadeboard. This is the model that was the most optimal given our chosen optimization metric. Let's star it. To start evaluating it, we can click on the model which will present use with the following options: Evaluate, Understand, Describe, and Predict (​additional tabs may be present based on extra features that are enabled).\n\nBefore we deep dive on each tab let's go to the Understand tab \u003E Feature Effects and click 'Compute Feature Effects'\n\nDataRobot offers superior transparency, interpretability, and explainability to help you better understand how models were built and give you the confidence to explain to others why a model made the predictions it did. If we go back and click the “D​escribe”​ tab, you can view the end-to-end model blueprint containing details of the specific feature engineering tasks and algorithms DataRobot used to run the model. In addition to an automated compliance report (In non trial accounts)\n\nIn non trial accounts this flow can be customized - Composable ML provides a full-flexibility approach to model building, allowing you to direct your data science and subject matter expertise to the models you build. With Composable ML, you build blueprints that best suit your needs using built-in tasks and custom Python/R code.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr9.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nClick on \"Evaluate\". The “Evaluate” option includes: Lift Chart, ROC Curve (for classification models), Confusion Matrix, Feature Fit, and Advanced Tuning.\n\nThe Lift Chart depicts how well a model segments the target population and how capable it is of predicting the target, letting you visualize the model's effectiveness. Looking at the Lift Chart, the left side of the curve indicates where the model predicted a low score on one section of the population while the right side of the curve indicates where the model predicted a high score. In general, the steeper the actual line is, and the more closely the predicted line matches the actual line, the better the model is. A consistently increasing line is another good indicator.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr7.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe ROC Curve tab helps to explore classification, performance, and statistics related to the selected model at any point on the probability scale. What that means is, at what probability of someone leaving do we raise an alarm? 30% chance? 50% chance? By play with different scenarios, we let get to see the outcomes of different scenarios. This tab contains a set of interactive graphical displays that include an ROC curve, a prediction distribution, cumulative charts, and a confusion matrix. There is also a display of summary statistics that describe model performance at the selected threshold. Each of these components combine to provide an interactive snapshot of the behavior of a model's classification power.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr8.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\nIn the “U​nderstand”​ tab, popular exploratory capabilities include Feature Impact, Feature Effects, Prediction Explanations, and Word Cloud (depending on the features in the dataset). These all help enlighten you on what drives a model’s predictions.\n\nDataRobot uses permutation importance to estimate feature impact. “Feature Impact” measures how much each feature contributes to the overall accuracy of the model. For this example, we can see that the most impactful reason a person is likely to stay as a customer or leave is the number of `CUSTOMER_SERVICE_CALLS`. The real question is, now how do we go reduce that number :) so that less people leave. This is just another way to take information from the machine learning lifecycle to add value to a business.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr10.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe “Feature Effects” chart displays model details on a per-feature basis (a feature's effect on the overall prediction). This gives you insight into understanding the relationship between each variable and the target. The insight is communicated in terms of partial dependence, which illustrates how a change in a feature's value, while keeping all other features as they were, impacts a model's predictions. Here we can see that once the number of `CUSTOMER_SERVICE_CALLS` goes above 3, the likelihood of the customer leaving rapidly increases.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr11.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n“Prediction Explanations” reveal the reasons why DataRobot generated a particular prediction for a certain data point. This gives you can ammunition to back up decisions with specific details. They provide a quantitative indicator of a variable's effect on an individual prediction.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr12.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nLastly, DataRobot’s \"Insights\" tab at the top provides more graphical representations of your model. There are tree-based variable rankings, hotspots, variable effects to illustrate the magnitude and direction of a feature's effect on a model's predictions, text mining charts, anomaly detection, and a word cloud of keyword relevancy.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr14.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\n\u003C!-- ------------------------ --\u003E\n## Deploying our model and using Job Defintions with Snowflake\n\nEvery model built in DataRobot is immediately ready for deployment. And there a several methods of scoring new data against this Deployment\n\nYou can: upload a new dataset to be scored in batch, create a REST API endpoint to score data directly from applications, export the model for in-place database scoring, or use a partner integration. Today we will use the Job Definitions feature.\n\nIf you click the number in the \"Prediction Threshold\" box, you can see a few different easy options to set your threshold. Again, at what probability do we say, \"Yes this person is going to churn.\" Choose \"Maximize F1\". Then click on the “Deploy Model” button.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr15.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nHere we are presented with a bunch of options to tweak our deployment.\nThe model can be deploy to any prediction server - even deploy to Snowflake\n\nScroll down to the main section \"Association ID.\" Here we want to type in `CUST_ID`. This is going to allow use to track predictions by customer when we go to monitor our model.\n\nThen go ahead and toggle the \"Require association ID in prediction requests\" and the 4 other option all under \"Data Drift.\"\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr16.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nScroll back up. We will see all of our boxes are green except for 'Continuous AI'\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr17.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nTo maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. (we won't test it today)\n\nClick \"Create Deployment\"\n\nThis will present us with a screen to give a priority setting to this deployment. We can skip and click \"Create deployment.\" This may take a couple of minutes to create the deployment.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr18.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nAfter creating the new deployment - click on 'return to deployments'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p39.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe Deployments Inventory shows the real-time status of all deployed models, regardless of how they were created or when and where they were deployed.. Here you can manage, monitor, and govern your deployed models and seeing at a glance which models are stale, preventing risk to your business.\n\nAnd now we have one active deployment. Go ahead and click on it.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr19.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe have different capabilities as part of MLOps, including service health, data drift, accuracy, Embedded Governance, Humility, Fairness and more, that we won't cover today.\n\nGo to the \"Predictions\" tab then \"Job Definitions\" tab and click on \"+Add job definition\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr20.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe will start setting up our job definition.\n\nclick on '+ Define connection' next to the Prediction source and choose the 'Snowflake HOL' connection and your user.\nOn the Search by: choose Tables and type 'scoring' - you will see the table 'SCORING_DATA', select this table and click 'Save connection'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr21.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe \"Prediction Source\" is now complete. You can adjust the prediction options if needed, for this walkthrough we're going to stick with the defaults.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr22.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe last section to complete is the \"Prediction Destination\". Go ahead and leave the Destination Type as \"JDBC\", and click \"+ Define Connection\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr23.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nRepeat the process as before in the \"Prediction Source\" (choose the data connection) and on tables create a new table\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr26.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nGo ahead and click “Create a table”\nSchema: 'CUSTOMER_DATA.PUBLIC'\nname: SCORED_DATA' and click 'Save connection'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr27.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nChange the write strategy to 'Insert'.\n\nAt the bottom you can schedule this job to run on a Schedule, or just run it manually. Go ahead and click \"Save Prediction job definition\" in the bottom left, then click on \"View all Job Definitions\" in the upper left. Click the hamburger icon on the right side of the job definition you just made, and click \"Run now\".\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr29.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nIf you click the hamburger icon again and go to “View job history” you can see its status. Once it's finished you should see the green “Succeeded” message under status.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr30.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nWe can now finally head back to our Snowflake tab. If you hit the refresh icon near the top left of our screen by your databases, you should see the `SCORED_DATA` table that was created once we kicked off our prediction job. If you excecute the query:\n\n```sql\nSELECT\n    *\nFROM\n    customer_data.public.scored_data;\n```\n\n\nYou will see our data with three new columns: 'CHURN_True_Prediction', 'CHURN_False_Prediction' showing the likelihood that each person is likely to churn or stay, and the decision in the column 'CHURN_PREDICTION'\n\nClick on the 'CHURN_PREDICTION'column, on the right side, we can see that snowflake snowsight presents statistics - In this case, looks like 430 (which is 17.5%) of the customers for this dataset are likely to churn. It also offers additional charts for further analysis.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/dr25.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nAnd as they say, that is all folks!\n\nFor the advanced python users you can keep the lab to see how to use python for snowpark togetehr with DataRobot.\n\n## (Extra) Connecting Snowflake with Zepl - DataRobot notebooks in the cloud\n\n\nGo back to the partner connect section in Snowflake and click the \"Data Science & ML\" category and click \"Zepl\"\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p62.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThis will present you with a screen to connect to Zepl. 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 click \"Connect\" .\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p63.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou then will be prompted to activate your account now, click the blue \"Activate\" button to do so.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p64.png)\n\nThis will launch a new tab to the Zepl platform.\n\u003C!-- ------------------------ --\u003E\n## Getting Started with Zepl - DataRobot notebooks in the cloud\n\nDataRobot Zepl the cloud data science notebook solution that enables advanced data scientists to do exploratory, code-centric work in Python, R, and Scala with enterprise features such as collaboration, versioning, and security.\n\nIn this exercise, we will use snowpark python and datarobot zepl notebook to do some EDA and visualizatoion\n\nFirst, let's configure Zepl:\nAfter creating your zepl account through the partner connect download the following notebook:\n\n\u003Cbutton\u003E\n\n  [Download the notebook](https://app.zepl.com/viewer/notebooks/bm90ZTovL2F0YWxpYS5ob3JlbnNodGllbkBkYXRhcm9ib3QuY29tLzAxYjY3NjdiMDk0ZDQwZjE5YmI1Njg1MjJlODI2MDJjL25vdGUuanNvbg)\n\u003C/button\u003E\n\nClick on my My Notebooks\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p66.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nImport the notebook: click import on the right side, upload the notebook that was preshared and once the notebook appears in your screen click on the notebook name: Snowflake_HOL\n\n\n\u003C!-- ------------------------ --\u003E\n## Creating a Zepl Data Connection\n\nTo create a data connection from a notebook do the following:\n1. Open the data sources sidebar by clicking the data sources icon on the right side of the notebook\n2. Click the Add new button at the top of the sidebar and select 'Secret store'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p42.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n3. Fill in the fields:\n\na. NAME: 'Snowflake_DR_HOL'\n\nb. KEY VALUES:\n\n  Key: 'password'\n\n  Value: your snowflake account password\n\nAnd click on the button 'Add'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p41.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nYou will see the new secret under the 'Attached to this notebook'\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p67.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\u003C!-- ------------------------ --\u003E\n## Connect to Snowflake Snowpark\n\nDownload the Snowpark library:\n\u003Cbutton\u003E\n\n  [Download the Snowpark library](https://drive.google.com/file/d/15kVt0XtGIKl7CrN27jpUHwcW4MvdIdiA/view?usp=sharin)\n\u003C/button\u003E\n\nTo import the Snowpark library:\n1. Open the folder sidebar by clicking the folder icon on the right side of the notebook\n2. Import the Snowpark library that was preshared\n3. When you see the file, hover on the file and click the symbol '\u003C/\u003E' and copy the path\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p69.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n4. Modify Section1:\n\n-Replace the path in line1 with the copied path\n\n-Change the id number in line2 - the line id is teh id from the path\n\nNow you can run in the notebook Sections 1-2:\n\nSection 1 will download and install the snowpark python package\n\nSection 2 will import the necessery packages\n\n5. Modify Section3:\n\naccount: change to your Snowflake account name (WITHOUT snowflakecomputing.com)\n\nuser: change to your user name\n\nNow you can run Sections 3-4:\n\nSection 3 will connect to snowflake where we created the training table\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p68.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nSection 4 will present the data in the training table we created in snowflake, query is running in Snowflake\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p70.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\u003C!-- ------------------------ --\u003E\n## EDA on Zepl notebook using python for snowpark\n\nIn the notebook, run Section 5\nThis section will create a new feature to identify if a user is more active at night or day.\nThat can contribute to our model, yo understand things like - why this user churn, when to send this use promotions, etc.\nusing python for snowpark, using the snowflake compute.\nThe results will be overwrtie the train table in Snowflake\n\n\nGo back to your snowflake account and run again the sql:\n\n```sql\nSELECT\n    *\nFROM\n    train_data\nLIMIT 10;\n```\n\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/automl-with-snowflake-and-datarobot/p71.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\u003C!-- ------------------------ --\u003E\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-850581a368","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-075d8972fd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3dddaff9e3","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-f3efa7d3d9","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 instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-5bfa691f10",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-daa28848d8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-d4526055e8","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-e1c8364157","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/automl-with-snowflake-and-datarobot",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EUse Case Overview\u003C/h2\u003E","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E","\u003Ch2\u003ECreating a Snowflake Database\u003C/h2\u003E","\u003Ch2\u003ECreating a Snowflake Table\u003C/h2\u003E","\u003Ch2\u003ECreating a Snowflake External Stage\u003C/h2\u003E","\u003Ch2\u003ECreating a Snowflake File Format\u003C/h2\u003E","\u003Ch2\u003ELoading Data into Snowflake\u003C/h2\u003E","\u003Ch2\u003EConnecting Snowflake with DataRobot\u003C/h2\u003E","\u003Ch2\u003EGetting Started with DataRobot\u003C/h2\u003E","\u003Ch2\u003ECreating a DataRobot Data Connection\u003C/h2\u003E","\u003Ch2\u003ESetting up a DataRobot Project\u003C/h2\u003E","\u003Ch2\u003EStarting DataRobot Quick Autopilot\u003C/h2\u003E","\u003Ch2\u003EEvaluating the \"Recommended For Deployment\" Model\u003C/h2\u003E","\u003Ch2\u003EDeploying our model and using Job Defintions with Snowflake\u003C/h2\u003E","\u003Ch2\u003E(Extra) Connecting Snowflake with Zepl - DataRobot notebooks in the cloud\u003C/h2\u003E","\u003Ch2\u003EGetting Started with Zepl - DataRobot notebooks in the cloud\u003C/h2\u003E","\u003Ch2\u003ECreating a Zepl Data Connection\u003C/h2\u003E","\u003Ch2\u003EConnect to Snowflake Snowpark\u003C/h2\u003E","\u003Ch2\u003EEDA on Zepl notebook using python for snowpark\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-36b24d3b1b","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/automl-with-snowflake-and-datarobot",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-b5c4bed8f0","title":"Page 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