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Overview\u003C/h2\u003E\n","\u003Cp\u003EContinual is the Operational AI layer for the \u003Ca href=\"https://continual.ai/\"\u003EModern Data Stack\u003C/a\u003E with Snowflake as the foundation. This guide is a simple introduction that will cover connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining ML models continuously over time. After completing this guide, there are \u003Ca href=\"https://docs.continual.ai/customer-churn/\"\u003Emore advanced examples\u003C/a\u003E to try with other Modern Data Stack technologies like \u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/1_5.png\" alt=\"1_5\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo keep things simple, we’ll use a nicely manicured dataset to illustrate how Snowflake and Continual team up to enable modern data teams to effectively build, deploy, and utilize production grade ML models. The dataset consists of customer information such as account data, demography, geographic area, and phone activity of a fictional telecommunications business. It also contains a boolean value defining whether or not a customer has ended their contract and “churned”. While this dataset will suffice the purposes of quickly trying Continual and Snowflake, we don’t believe it's the most realistic example, which is why we created a more comprehensive \u003Ca href=\"https://docs.continual.ai/customer-churn/\"\u003Eexample you can try next\u003C/a\u003E!\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic experience with Snowflake and SQL\u003C/li\u003E\u003Cli\u003EBasic knowledge of ML and data science problems\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EYou'll Learn\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EHow to connect Continual to Snowflake and do ML on your data cloud\u003C/li\u003E\u003Cli\u003ECreate feature sets and models in Continual\u003C/li\u003E\u003Cli\u003EEvaluate and maintain production ML models\u003C/li\u003E\u003Cli\u003EAnalyze model performance, input data, and features to iteratively improve performance\u003C/li\u003E\u003Cli\u003EWrite predictions to Snowflake\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EYou’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://cloud.continual.ai/register?utm_campaign=Snowflake%20QuickStarts&utm_source=continual%20guide\"\u003ESign up for a Continual account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EYou’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn operational, continually updating customer churn ML model\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EPrepare your lab environment\u003C/h2\u003E\n","\u003Ch3\u003ESet up Snowflake\u003C/h3\u003E\n","\u003Cp\u003ELogin using your unique credentials if you have a Snowflake account. If you don't have a Snowflake account, visit \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Ehttps://signup.snowflake.com\u003C/a\u003E and sign up for a free 30-day trial environment.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/1_start_snowflake_trial.png\" alt=\"new_trial\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor this example, you will only need the Standard edition on \u003Cstrong\u003EAWS\u003C/strong\u003E. But you may want to select \u003Cstrong\u003EEnterprise\u003C/strong\u003E to try out rad features like time travel, materialized views, or database failover.\u003C/p\u003E\n","\u003Cp\u003EChoose \u003Cstrong\u003EUS West (Oregon)\u003C/strong\u003E for the AWS region.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/2_snowflake_trial_signup.png\" alt=\"2_new_trial\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/3.png\" alt=\"3\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you've logged in, open a new \u003Cstrong\u003EWorksheet\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/4.png\" alt=\"4\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/5.png\" alt=\"5\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/6.png\" alt=\"6\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/7.png\" alt=\"7\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate a role, user, warehouse and database for Continual to use.\u003C/p\u003E\n","\u003Cp\u003EIn Worksheets, \u003Cstrong\u003Ecopy and paste the following SQL\u003C/strong\u003E into your worksheet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EACTION REQUIRED:\u003C/strong\u003E Make sure to update the user_password\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ebegin; \n\n-- ACTION NEEDED: choose a password for CONTINUAL_USER.\nset user_password = 'REPLACE ME WITH A SECURE PASSWORD';\nset role_name = 'CONTINUAL_ROLE';\nset user_name = 'CONTINUAL_USER';\nset warehouse_name = 'CONTINUAL_WAREHOUSE';\nset database_name = 'CONTINUAL'; \n\n-- change role to securityadmin for user / role steps\nuse role securityadmin; \n\n-- create role for Continual\ncreate role if not exists identifier($role_name);\ngrant role identifier($role_name) to role SYSADMIN;\n\n-- create a user for Continual\ncreate user if not exists identifier($user_name)\npassword = $user_password\ndefault_role = $role_name\ndefault_warehouse = $warehouse_name; \n\ngrant role identifier($role_name) to user identifier($user_name);\n\n-- change role to sysadmin for warehouse / database steps \n\nuse role sysadmin; \n\n-- create a warehouse for Continual\ncreate warehouse if not exists identifier($warehouse_name)\nwarehouse_size = medium\nwarehouse_type = standard\nauto_suspend = 10\nauto_resume = true\ninitially_suspended = true; \n\n-- create database for Continual\ncreate database if not exists identifier($database_name); \n\n-- grant Continual role access to warehouse\ngrant USAGE\non warehouse identifier($warehouse_name)\nto role identifier($role_name);\n\n-- grant Continual access to database\ngrant CREATE SCHEMA, MONITOR, USAGE\non database identifier($database_name)\nto role identifier($role_name); \n\nCommit;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/8.png\" alt=\"8\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOutside of what we defined above, we will not use additional databases/schemas/tables as sources for feature sets or models in this example. But for an actual use case, you will likely use additional sources and will need to grant the Continual user USAGE permission on any such resources. See \u003Ca href=\"https://docs.continual.ai/snowflake/#snowflake\"\u003Eour docs\u003C/a\u003E for more information.\u003C/p\u003E\n","\u003Ch2\u003ESet up Continual\u003C/h2\u003E\n","\u003Ch3\u003ESignup for a trial account\u003C/h3\u003E\n","\u003Cp\u003ETo get started, navigate to \u003Ca href=\"https://cloud.continual.ai/register?utm_campaign=Snowflake%20QuickStarts&utm_source=continual%20guide\"\u003EContinual.ai\u003C/a\u003E and register an account. Continual has a free 30-day trial and no credit card is required.\u003C/p\u003E\n","\u003Cp\u003EYou’ll need to verify your email address. If you don’t receive a verification email within a few minutes, check your spam folder and email support@continual.ai. If your link expires, you can log back into your account to send a new verification email.\u003C/p\u003E\n","\u003Ch3\u003ECreate an organization\u003C/h3\u003E\n","\u003Cp\u003EOrganizations allow you to share projects within a company and collaborate with team members under a shared billing account.\u003C/p\u003E\n","\u003Ch3\u003ECreate a project\u003C/h3\u003E\n","\u003Cp\u003EAfter creating your organization you will see your organization's project dashboard with the option to create a project. Projects are isolated workspaces for feature sets and models and connect bi-directionally with Snowflake.\u003C/p\u003E\n","\u003Cp\u003EGo ahead and create a new project and name it \u003Cstrong\u003ECustomerChurn\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/9.png\" alt=\"9\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EConnect Continual to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EEach Continual project connects bi-directionally to one Snowflake Database. Continual maintains tables and views for all your feature sets, models, and model predictions inside a schema. This makes it easy to build models from your existing data and consume the predictions Continual maintains using your existing tools in Snowflake!\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EConnect your data warehouse\u003C/strong\u003E and then select \u003Cstrong\u003ESnowflake\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/10.png\" alt=\"10\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/11.png\" alt=\"11\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter your snowflake account identifier, username, password, database name, warehouse name, and role. Leave the schema field blank for Continual to automatically create one.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E The Host (Endpoint) is the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers\"\u003ESnowflake account identifier\u003C/a\u003E. If you selected a region other than US West (Oregon) you need \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers\"\u003Eadditional segments depending on the region\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/12.png\" alt=\"12\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETest the connection and then create the connection between Continual and Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/13.png\" alt=\"13\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ECreate a feature set\u003C/h2\u003E\n","\u003Cp\u003ENow that we’ve established our connection and can access data in Snowflake, it’s time to prepare features for a model.\u003C/p\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://docs.continual.ai/feature-sets/\"\u003Efeature set\u003C/a\u003E is one of the main objects in Continual and describes a collection of related features. Feature sets are defined by a SQL query in a YAML configuration file. Continual uses this query to build a view in your feature store corresponding to the feature set query definition.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate a feature set\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/14.png\" alt=\"14\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe first step in creating a feature set is the Query Data step. This is where we use SQL to select the data defining our feature set. To make it easy, we have an example ready to go that will copy a csv from an object store into your Snowflake database and pre-populate the query editor, configurations and metadata, and schema. You are living the good life!\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EUse an Example\u003C/strong\u003E on the right-hand side and select \u003Cstrong\u003EPredict Customer Churn\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/15.png\" alt=\"15\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPreview\u003C/strong\u003E the data to verify the query is selecting the data required for the feature set.\u003C/p\u003E\n","\u003Cp\u003EThen select \u003Cstrong\u003EConfigure Feature Set\u003C/strong\u003E on the bottom right to advance to the next step.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/16.png\" alt=\"16\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EConfigure Feature Set\u003C/strong\u003E step is where you add the metadata to the feature set: name, description, entity, and index. An \u003Ca href=\"https://docs.continual.ai/feature-sets/\"\u003Eentity\u003C/a\u003E is a higher level object that combines feature sets that represent common business objects such as "customers", "products", and "sales". The \u003Ca href=\"https://docs.continual.ai/feature-sets/#feature-set\"\u003Eindex\u003C/a\u003E is what uniquely identifies the feature set and connects it to an entity. All feature sets in an entity have the same index.\u003C/p\u003E\n","\u003Cp\u003EPopulate the fields as shown below and create a new entity called \u003Cstrong\u003Ecustomer\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/17.png\" alt=\"17\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EDefine Schema\u003C/strong\u003E to advance to the next step.\u003C/p\u003E\n","\u003Cp\u003ENotice our feature set is displayed in the Data Model graph with all the columns, data types, and inclusion status.\u003C/p\u003E\n","\u003Cp\u003EOkay, time to review and create! Click \u003Cstrong\u003EReview Changes\u003C/strong\u003E and then \u003Cstrong\u003ESubmit Changes\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/18.png\" alt=\"18\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, click on the \u003Cstrong\u003EChanges\u003C/strong\u003E tab on the left hand side to see the action added to the activity feed:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/19.png\" alt=\"19\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the Feature Set has been created, we can see it listed on \u003Cstrong\u003EFeature Sets\u003C/strong\u003E on the left vertical menu:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/20.png\" alt=\"20\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ECreate a model\u003C/h2\u003E\n","\u003Cp\u003EIn the last section, we connected to Snowflake and created a feature set for a ML model. Now it’s time to create a model that will ingest our feature set, along with a few additional individual features, to predict the probability of a customer churning. The flow is very similar to creating a feature set except with some additional configurations.\u003C/p\u003E\n","\u003Cp\u003EUnlike when creating a feature set, at the configuration step, we’ll need to provide a target column to train our model on. Then we'll set policies for re-training, promotion, and running predictions. Click on \u003Cstrong\u003EModels\u003C/strong\u003E on the left hand side, then \u003Cstrong\u003ECreate Model\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/21.png\" alt=\"21\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EUse an example\u003C/strong\u003E and then select \u003Cstrong\u003EPredict Customer Churn\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/22.png\" alt=\"22\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to make sure our SQL query contains a unique index, features, and a target. In addition to new features we’ll define in our model spine, we want to include the feature set we built in the last section. The way we include our feature set is by including the index column of our feature set in our query and then linking it to our “customers” entity in the \u003Cstrong\u003EReview Schema\u003C/strong\u003E step. Then, at model training time, Continual will join the feature set with the model to create the training data set. \u003C/p\u003E\n","\u003Cp\u003EWe typically recommend storing your features in feature sets and connecting them to your models via \u003Ca href=\"https://docs.continual.ai/feature-sets/?h=entity+linking#entity\"\u003Eentity linking\u003C/a\u003E, but it's also possible to specify a list of columns in your model that represent additional features to bring into the model.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EConfigure Model\u003C/strong\u003E:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/23.png\" alt=\"23\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECool, so let’s give our model a name and description and define our model index and target column. These attributes, along with a sql query that generates the data and linked entities, forms the core of a model definition, and this is sometimes referred to as the model spine.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EDefine Schema\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/24.png\" alt=\"24\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow it’s time to link our feature set index to our "customer" entity. Click the chain icon on the \u003Cstrong\u003Eid\u003C/strong\u003E row and then select \u003Cstrong\u003Ecustomer\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/25.png\" alt=\"25\"\u003E\u003C/p\u003E\n","\u003Cp\u003EType "customer" into the pop up box:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/26.png\" alt=\"26\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen click \u003Cstrong\u003ELink Column\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/27.png\" alt=\"27\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ESet Policies\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/28.png\" alt=\"28\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Continual, you can configure recurring training schedules to ensure your model is updating as frequently as it needs to. You can also set advanced settings such as which performance metric to optimize for, the size of the container, and even which models to include or exclude in the experiment.  While automated, Continual allows you to have control over how your model is created, optimized, deployed, and managed.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.continual.ai/data_checks/?h=data+checks\"\u003EData checks\u003C/a\u003E, \u003Ca href=\"https://docs.continual.ai/mlops/?h=data+profiling#data-profiling\"\u003EData Profiling\u003C/a\u003E, and other automated capabilities are enabled by default. But for additional analysis such as \u003Ca href=\"https://docs.continual.ai/xai/?h=shapley#shapley-values-global\"\u003EShapley values\u003C/a\u003E, let's toggle \u003Cstrong\u003EAdditional Plots\u003C/strong\u003E to \u003Cstrong\u003EOn\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/shapley.png\" alt=\"shapley\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can also set how the system chooses which model to promote to production and when new predictions should be made.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/30.png\" alt=\"30\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGo ahead and create the model by clicking \u003Cstrong\u003ESubmit Changes\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/31.png\" alt=\"31\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWell done! How easy was that?\u003C/p\u003E\n","\u003Cp\u003EAll changes you make in Continual, such as creating a new feature set or editing/updating an existing model, is listed in the \u003Cstrong\u003EChanges\u003C/strong\u003E tab. This gives you a lineage of your team’s work you can reference at any time.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/32.png\" alt=\"32\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce your model has been created and promoted it will write predictions directly back to Snowflake. Continual creates a table in your feature store for every model you create in the system that tracks all predictions made by model versions in that model over time. This table lives under <feature_store>.<project_id>.model_<model_id>\u003Cem\u003Epredictions_history. Continual additionally builds a view under <feature_store>.<project_id>.model\u003C/em\u003E<model_id>_predictions which represents the latest prediction made for each record in your model spine.\u003C/p\u003E\n","\u003Cp\u003ELet’s use the latest predictions view. In Snowflake, paste the following sql statement in to view all your predictions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM continual.customerchurn.model_customer_churn_30days_predictions; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/33.png\" alt=\"33\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/34.png\" alt=\"34\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EMLOps: Monitoring data and models\u003C/h2\u003E\n","\u003Cp\u003EBack in Continual, there are many tools and automation for monitoring data, models, and prediction jobs.\u003C/p\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EModels\u003C/strong\u003E and select the customer_churn_30days model:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/35.png\" alt=\"35\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEach time you train a model, a new version is produced and managed under the \u003Cstrong\u003EModel Version\u003C/strong\u003E view.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EVersions\u003C/strong\u003E and choose a Model Version to evaluate:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/36.png\" alt=\"36\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPerformance Analysis\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EOverview\u003C/strong\u003E page shows the performance of the winning model, as well as each model that was tested. Continual runs a series of experiments across different model algorithms and optimizes performance across a specified performance metric.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/37.png\" alt=\"37\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EMonitoring Data\u003C/h3\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EData Analysis\u003C/strong\u003E to look closer at the data used to train the model.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/38.png\" alt=\"38\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHere you can look at the \u003Ca href=\"https://docs.continual.ai/mlops/#correlation-matrix\"\u003Ecorrelation matrix\u003C/a\u003E to see which two variables are correlated and \u003Ca href=\"https://docs.continual.ai/mlops/#data-profiling\"\u003Ecategory scores\u003C/a\u003E to look at each feature’s profile to check if there are features with many Null values, large outliers, or unexpected distributions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/39.png\" alt=\"39\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAnalyzing the model\u003C/h3\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EModel Insights\u003C/strong\u003E and look at the confusion matrix to understand what your model is getting right and what types of errors it’s making.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/40.png\" alt=\"40\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can also reference Feature Importance to view which features were the most impactful. Continual performs \u003Ca href=\"https://scikit-learn.org/stable/modules/permutation_importance.html\"\u003Epermutation-based\u003C/a\u003E feature importance on the winning experiment and is available for each model version.\u003C/p\u003E\n","\u003Cp\u003ELet's take a look at \u003Ca href=\"https://docs.continual.ai/xai/?h=shapley#shapley-values-global\"\u003Eshapley values\u003C/a\u003E for insights about how each feature affects a prediction with regard to its expected value:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/405.png\" alt=\"405\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EGetting Started with a Use Case\u003C/h2\u003E\n","\u003Cp\u003EJust like that you’ve enabled ML on Snowflake. Continual is the Operational AI layer for the modern data stack and designed with the shared principles of simplicity, minimal management overhead, and elasticity.\u003C/p\u003E\n","\u003Cp\u003EIn less than 20 minutes, we connected Continual to Snowflake, created a feature set, used it as input to experiment among more than 10 ML models and added other relevant features, promoted the best performing model to production, wrote prediction results back to Snowflake, analyzed our features and model performance to learn what improvements we can make. We completed our work in the UI but could’ve used the CLI or SDK.\u003C/p\u003E\n","\u003Cp\u003EThis concludes the guide to quickly getting started with Continual on Snowflake. Now you’re ready for a \u003Ca href=\"https://docs.continual.ai/customer-churn/\"\u003Emore advanced example of predicting customer churn\u003C/a\u003E with Continual and dbt on Snowflake. If you need a little help or have questions along the way, \u003Ca href=\"https://continual.ai/book-a-demo?uisource=Snowflake-Quickstart-Guide\"\u003Ebook some time\u003C/a\u003E with one of our AI experts.  You can also learn more and see a demo of Continual on Snowflake at our recent webinar replay, available \u003Ca href=\"https://continual.ai/events/the-new-ai-dream-teams\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E"],"description":"Operationalize AI faster with Continual's AutoML platform and Snowflake for automated prediction pipelines and ML deployment.","title":"A Faster Path to Operational AI with Continual and Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Guide Overview\n\nContinual is the Operational AI layer for the [Modern Data Stack](https://continual.ai/) with Snowflake as the foundation. This guide is a simple introduction that will cover connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining ML models continuously over time. After completing this guide, there are [more advanced examples](https://docs.continual.ai/customer-churn/) to try with other Modern Data Stack technologies like [dbt](https://www.getdbt.com/).\n\n![1_5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/1_5.png)\n\nTo keep things simple, we’ll use a nicely manicured dataset to illustrate how Snowflake and Continual team up to enable modern data teams to effectively build, deploy, and utilize production grade ML models. The dataset consists of customer information such as account data, demography, geographic area, and phone activity of a fictional telecommunications business. It also contains a boolean value defining whether or not a customer has ended their contract and “churned”. While this dataset will suffice the purposes of quickly trying Continual and Snowflake, we don’t believe it's the most realistic example, which is why we created a more comprehensive [example you can try next](https://docs.continual.ai/customer-churn/)! \n\n### Prerequisites\n- Basic experience with Snowflake and SQL\n- Basic knowledge of ML and data science problems\n\n### You'll Learn\n1. How to connect Continual to Snowflake and do ML on your data cloud\n2. Create feature sets and models in Continual\n3. Evaluate and maintain production ML models\n4. Analyze model performance, input data, and features to iteratively improve performance\n5. Write predictions to Snowflake \n\n### You’ll Need \n- [Sign up for a Continual account](https://cloud.continual.ai/register?utm_campaign=Snowflake%20QuickStarts&utm_source=continual%20guide)\n- [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n\n### You’ll Build \n- An operational, continually updating customer churn ML model\n\n## Prepare your lab environment\n### Set up Snowflake\n\nLogin using your unique credentials if you have a Snowflake account. If you don't have a Snowflake account, visit [https://signup.snowflake.com](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) and sign up for a free 30-day trial environment.\n![new_trial](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/1_start_snowflake_trial.png)\n\nFor this example, you will only need the Standard edition on **AWS**. But you may want to select **Enterprise** to try out rad features like time travel, materialized views, or database failover. \n\nChoose **US West (Oregon)** for the AWS region. \n![2_new_trial](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/2_snowflake_trial_signup.png)\n![3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/3.png)\n\nOnce you've logged in, open a new **Worksheet**. \n![4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/4.png)\n![5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/5.png)\n![6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/6.png)\n![7](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/7.png)\n\nCreate a role, user, warehouse and database for Continual to use. \n\nIn Worksheets, **copy and paste the following SQL** into your worksheet. \n\n**ACTION REQUIRED:** Make sure to update the user_password\n\n~~~sql\nbegin; \n\n-- ACTION NEEDED: choose a password for CONTINUAL_USER.\nset user_password = 'REPLACE ME WITH A SECURE PASSWORD';\nset role_name = 'CONTINUAL_ROLE';\nset user_name = 'CONTINUAL_USER';\nset warehouse_name = 'CONTINUAL_WAREHOUSE';\nset database_name = 'CONTINUAL'; \n\n-- change role to securityadmin for user / role steps\nuse role securityadmin; \n\n-- create role for Continual\ncreate role if not exists identifier($role_name);\ngrant role identifier($role_name) to role SYSADMIN;\n\n-- create a user for Continual\ncreate user if not exists identifier($user_name)\npassword = $user_password\ndefault_role = $role_name\ndefault_warehouse = $warehouse_name; \n\ngrant role identifier($role_name) to user identifier($user_name);\n\n-- change role to sysadmin for warehouse / database steps \n\nuse role sysadmin; \n\n-- create a warehouse for Continual\ncreate warehouse if not exists identifier($warehouse_name)\nwarehouse_size = medium\nwarehouse_type = standard\nauto_suspend = 10\nauto_resume = true\ninitially_suspended = true; \n\n-- create database for Continual\ncreate database if not exists identifier($database_name); \n\n-- grant Continual role access to warehouse\ngrant USAGE\non warehouse identifier($warehouse_name)\nto role identifier($role_name);\n\n-- grant Continual access to database\ngrant CREATE SCHEMA, MONITOR, USAGE\non database identifier($database_name)\nto role identifier($role_name); \n\nCommit;\n~~~\n\n![8](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/8.png)\n\nOutside of what we defined above, we will not use additional databases/schemas/tables as sources for feature sets or models in this example. But for an actual use case, you will likely use additional sources and will need to grant the Continual user USAGE permission on any such resources. See [our docs](https://docs.continual.ai/snowflake/#snowflake) for more information.\n\n## Set up Continual\n### Signup for a trial account\nTo get started, navigate to [Continual.ai](https://cloud.continual.ai/register?utm_campaign=Snowflake%20QuickStarts&utm_source=continual%20guide) and register an account. Continual has a free 30-day trial and no credit card is required.\n\nYou’ll need to verify your email address. If you don’t receive a verification email within a few minutes, check your spam folder and email support@continual.ai. If your link expires, you can log back into your account to send a new verification email.\n\n### Create an organization\nOrganizations allow you to share projects within a company and collaborate with team members under a shared billing account.\n\n### Create a project\nAfter creating your organization you will see your organization's project dashboard with the option to create a project. Projects are isolated workspaces for feature sets and models and connect bi-directionally with Snowflake. \n\nGo ahead and create a new project and name it **CustomerChurn**\n\n![9](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/9.png)\n\n### Connect Continual to Snowflake\nEach Continual project connects bi-directionally to one Snowflake Database. Continual maintains tables and views for all your feature sets, models, and model predictions inside a schema. This makes it easy to build models from your existing data and consume the predictions Continual maintains using your existing tools in Snowflake! \n\nClick **Connect your data warehouse** and then select **Snowflake**\n\n![10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/10.png)\n![11](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/11.png)\n\nEnter your snowflake account identifier, username, password, database name, warehouse name, and role. Leave the schema field blank for Continual to automatically create one. \n\n\u003E **NOTE:** The Host (Endpoint) is the [Snowflake account identifier](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers). If you selected a region other than US West (Oregon) you need [additional segments depending on the region](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers).\n\n![12](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/12.png)\n\nTest the connection and then create the connection between Continual and Snowflake. \n\n![13](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/13.png)\n\n## Create a feature set\n\nNow that we’ve established our connection and can access data in Snowflake, it’s time to prepare features for a model. \n\nA [feature set](https://docs.continual.ai/feature-sets/) is one of the main objects in Continual and describes a collection of related features. Feature sets are defined by a SQL query in a YAML configuration file. Continual uses this query to build a view in your feature store corresponding to the feature set query definition. \n\nClick **Create a feature set**:\n\n![14](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/14.png)\n\nThe first step in creating a feature set is the Query Data step. This is where we use SQL to select the data defining our feature set. To make it easy, we have an example ready to go that will copy a csv from an object store into your Snowflake database and pre-populate the query editor, configurations and metadata, and schema. You are living the good life! \n\nClick **Use an Example** on the right-hand side and select **Predict Customer Churn**\n\n![15](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/15.png)\n\n**Preview** the data to verify the query is selecting the data required for the feature set. \n\nThen select **Configure Feature Set** on the bottom right to advance to the next step.\n\n![16](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/16.png)\n\nThe **Configure Feature Set** step is where you add the metadata to the feature set: name, description, entity, and index. An [entity](https://docs.continual.ai/feature-sets/) is a higher level object that combines feature sets that represent common business objects such as \"customers\", \"products\", and \"sales\". The [index](https://docs.continual.ai/feature-sets/#feature-set) is what uniquely identifies the feature set and connects it to an entity. All feature sets in an entity have the same index. \n\nPopulate the fields as shown below and create a new entity called **customer**. \n\n![17](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/17.png)\n\nClick **Define Schema** to advance to the next step. \n\nNotice our feature set is displayed in the Data Model graph with all the columns, data types, and inclusion status. \n\nOkay, time to review and create! Click **Review Changes** and then **Submit Changes**: \n\n![18](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/18.png)\n\nNow, click on the **Changes** tab on the left hand side to see the action added to the activity feed:\n\n![19](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/19.png)\n\nOnce the Feature Set has been created, we can see it listed on **Feature Sets** on the left vertical menu:\n\n![20](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/20.png)\n\n## Create a model\n\nIn the last section, we connected to Snowflake and created a feature set for a ML model. Now it’s time to create a model that will ingest our feature set, along with a few additional individual features, to predict the probability of a customer churning. The flow is very similar to creating a feature set except with some additional configurations. \n\nUnlike when creating a feature set, at the configuration step, we’ll need to provide a target column to train our model on. Then we'll set policies for re-training, promotion, and running predictions. Click on **Models** on the left hand side, then **Create Model**:\n\n![21](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/21.png)\n\nClick **Use an example** and then select **Predict Customer Churn**:\n\n![22](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/22.png)\n\nWe need to make sure our SQL query contains a unique index, features, and a target. In addition to new features we’ll define in our model spine, we want to include the feature set we built in the last section. The way we include our feature set is by including the index column of our feature set in our query and then linking it to our “customers” entity in the **Review Schema** step. Then, at model training time, Continual will join the feature set with the model to create the training data set. \n\nWe typically recommend storing your features in feature sets and connecting them to your models via [entity linking](https://docs.continual.ai/feature-sets/?h=entity+linking#entity), but it's also possible to specify a list of columns in your model that represent additional features to bring into the model. \n\nClick **Configure Model**: \n![23](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/23.png)\n\nCool, so let’s give our model a name and description and define our model index and target column. These attributes, along with a sql query that generates the data and linked entities, forms the core of a model definition, and this is sometimes referred to as the model spine.\n\nClick **Define Schema**: \n\n![24](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/24.png)\n\nNow it’s time to link our feature set index to our \"customer\" entity. Click the chain icon on the **id** row and then select **customer**:\n\n![25](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/25.png)\n\nType \"customer\" into the pop up box:\n\n![26](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/26.png)\n\nThen click **Link Column**:\n\n![27](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/27.png)\n\nClick **Set Policies**: \n\n![28](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/28.png)\n\nIn Continual, you can configure recurring training schedules to ensure your model is updating as frequently as it needs to. You can also set advanced settings such as which performance metric to optimize for, the size of the container, and even which models to include or exclude in the experiment.  While automated, Continual allows you to have control over how your model is created, optimized, deployed, and managed. \n\n[Data checks](https://docs.continual.ai/data_checks/?h=data+checks), [Data Profiling](https://docs.continual.ai/mlops/?h=data+profiling#data-profiling), and other automated capabilities are enabled by default. But for additional analysis such as [Shapley values](https://docs.continual.ai/xai/?h=shapley#shapley-values-global), let's toggle **Additional Plots** to **On**.\n\n![shapley](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/shapley.png)\n\nYou can also set how the system chooses which model to promote to production and when new predictions should be made.\n\n![30](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/30.png)\n\nGo ahead and create the model by clicking **Submit Changes**: \n\n![31](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/31.png)\n\nWell done! How easy was that? \n\nAll changes you make in Continual, such as creating a new feature set or editing/updating an existing model, is listed in the **Changes** tab. This gives you a lineage of your team’s work you can reference at any time. \n\n![32](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/32.png)\n\nOnce your model has been created and promoted it will write predictions directly back to Snowflake. Continual creates a table in your feature store for every model you create in the system that tracks all predictions made by model versions in that model over time. This table lives under \u003Cfeature_store\u003E.\u003Cproject_id\u003E.model_\u003Cmodel_id\u003E_predictions_history. Continual additionally builds a view under \u003Cfeature_store\u003E.\u003Cproject_id\u003E.model_\u003Cmodel_id\u003E_predictions which represents the latest prediction made for each record in your model spine. \n\nLet’s use the latest predictions view. In Snowflake, paste the following sql statement in to view all your predictions: \n~~~sql\nSELECT * FROM continual.customerchurn.model_customer_churn_30days_predictions; \n~~~\n![33](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/33.png)\n\n![34](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/34.png)\n\n## MLOps: Monitoring data and models\n\nBack in Continual, there are many tools and automation for monitoring data, models, and prediction jobs.\n\nNavigate to **Models** and select the customer_churn_30days model:\n\n![35](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/35.png)\n\nEach time you train a model, a new version is produced and managed under the **Model Version** view. \n\nClick **Versions** and choose a Model Version to evaluate:\n\n![36](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/36.png)\n\n### Performance Analysis\nThe **Overview** page shows the performance of the winning model, as well as each model that was tested. Continual runs a series of experiments across different model algorithms and optimizes performance across a specified performance metric. \n\n![37](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/37.png)\n\n### Monitoring Data\nClick on **Data Analysis** to look closer at the data used to train the model. \n![38](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/38.png)\n\nHere you can look at the [correlation matrix](https://docs.continual.ai/mlops/#correlation-matrix) to see which two variables are correlated and [category scores](https://docs.continual.ai/mlops/#data-profiling) to look at each feature’s profile to check if there are features with many Null values, large outliers, or unexpected distributions. \n\n![39](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/39.png)\n\n### Analyzing the model\n\nClick on **Model Insights** and look at the confusion matrix to understand what your model is getting right and what types of errors it’s making. \n\n![40](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/40.png)\n\nWe can also reference Feature Importance to view which features were the most impactful. Continual performs [permutation-based](https://scikit-learn.org/stable/modules/permutation_importance.html) feature importance on the winning experiment and is available for each model version.\n\nLet's take a look at [shapley values](https://docs.continual.ai/xai/?h=shapley#shapley-values-global) for insights about how each feature affects a prediction with regard to its expected value: \n\n![405](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/a-faster-path-to-operational-ai-with-continual-and-snowflake/405.png)\n\n## Getting Started with a Use Case\nJust like that you’ve enabled ML on Snowflake. Continual is the Operational AI layer for the modern data stack and designed with the shared principles of simplicity, minimal management overhead, and elasticity. \n\nIn less than 20 minutes, we connected Continual to Snowflake, created a feature set, used it as input to experiment among more than 10 ML models and added other relevant features, promoted the best performing model to production, wrote prediction results back to Snowflake, analyzed our features and model performance to learn what improvements we can make. We completed our work in the UI but could’ve used the CLI or SDK. \n\nThis concludes the guide to quickly getting started with Continual on Snowflake. Now you’re ready for a [more advanced example of predicting customer churn](https://docs.continual.ai/customer-churn/) with Continual and dbt on Snowflake. If you need a little help or have questions along the way, [book some time](https://continual.ai/book-a-demo?uisource=Snowflake-Quickstart-Guide) with one of our AI experts.  You can also learn more and see a demo of Continual on Snowflake at our recent webinar replay, available [here](https://continual.ai/events/the-new-ai-dream-teams).\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-61edc199fd","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-2d5cb073e8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-cec5425425","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-c7f2ba066c","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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