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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThe Snowflake Online Feature Store provides low-latency, key-based feature retrieval for real-time ML inference. This guide demonstrates how to build an end-to-end machine learning workflow using the Online Feature Store to predict taxi trip durations in New York City.\u003C/p\u003E\n","\u003Cp\u003EYou'll learn how to register entities and feature views, perform feature engineering, and use both online and offline stores for real-time inference.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account (non-trial) in AWS or Azure commercial regions\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python and SQL\u003C/li\u003E\u003Cli\u003EFamiliarity with machine learning concepts\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN access or equivalent permissions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up the Snowflake Feature Store\u003C/li\u003E\u003Cli\u003EHow to register entities and create feature views\u003C/li\u003E\u003Cli\u003EHow to enable online serving for low-latency inference\u003C/li\u003E\u003Cli\u003EHow to train an XGBoost model using feature store data\u003C/li\u003E\u003Cli\u003EHow to make real-time predictions using online features\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\u003C/li\u003E\u003Cli\u003EBasic understanding of Snowpark and Snowflake ML\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA complete feature store for taxi trip prediction\u003C/li\u003E\u003Cli\u003EOnline feature views for real-time inference\u003C/li\u003E\u003Cli\u003EAn XGBoost regression model\u003C/li\u003E\u003Cli\u003EA real-time prediction system using online features\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup and Data Preparation\u003C/h2\u003E\n","\u003Cp\u003EThis section covers environment setup, notebook upload, and loading the sample dataset for the feature store guide.\u003C/p\u003E\n","\u003Ch3\u003ERun the Setup Script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen Snowflake and navigate to <a href="https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=intro-to-online-feature-store-in-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">\u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003EWorkspaces\u003C/strong\u003E</a>\u003C/li\u003E\u003Cli\u003ECreate a new SQL file\u003C/li\u003E\u003Cli\u003ECopy and paste the following setup script\u003C/li\u003E\u003Cli\u003ERun the entire script as \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- ============================================================================\n-- Snowflake Setup Script for Online Feature Store Demo\n-- ============================================================================\n\nUSE ROLE ACCOUNTADMIN;\n\nSET USERNAME = (SELECT CURRENT_USER());\nSELECT $USERNAME;\n\n-- Set query tag for tracking\nALTER SESSION SET QUERY_TAG = '{"origin":"sf_sit-is", "name":"sfguide_intro_to_online_feature_store", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"sql"}}';\n\n-- ============================================================================\n-- SECTION 1: CREATE ROLE AND GRANT ACCOUNT-LEVEL PERMISSIONS\n-- ============================================================================\n\n-- Create role for Feature Store operations and grant to current user\nCREATE OR REPLACE ROLE FS_DEMO_ROLE;\nGRANT ROLE FS_DEMO_ROLE TO USER identifier($USERNAME);\n\n-- Grant account-level permissions\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT IMPORT SHARE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT EXECUTE MANAGED TASK ON ACCOUNT TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 2: SWITCH TO ROLE AND CREATE RESOURCES\n-- ============================================================================\n\nUSE ROLE FS_DEMO_ROLE;\n\n-- Create warehouse\nCREATE OR REPLACE WAREHOUSE FS_DEMO_WH\n    WAREHOUSE_SIZE = 'XSMALL'\n    AUTO_SUSPEND = 300\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'Warehouse for Feature Store demo';\n\n-- Create database and schema\nCREATE OR REPLACE DATABASE FEATURE_STORE_DEMO\n    COMMENT = 'Database for Feature Store with taxi trip prediction';\n\nCREATE OR REPLACE SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES\n    COMMENT = 'Schema for taxi features and online feature store';\n\n-- Use the created resources\nUSE WAREHOUSE FS_DEMO_WH;\nUSE DATABASE FEATURE_STORE_DEMO;\nUSE SCHEMA TAXI_FEATURES;\n\n-- Create stage for model assets with directory enabled\nCREATE OR REPLACE STAGE FS_DEMO_ASSETS\n    DIRECTORY = (ENABLE = TRUE)\n    COMMENT = 'Stage for storing model assets and data files';\n\n-- Grant full privileges on database and schema to the role\nGRANT ALL PRIVILEGES ON DATABASE FEATURE_STORE_DEMO TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON WAREHOUSE FS_DEMO_WH TO ROLE FS_DEMO_ROLE;\n\n-- Grant future privileges to handle dynamically created objects\nGRANT ALL PRIVILEGES ON FUTURE TABLES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE VIEWS IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE DYNAMIC TABLES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE STAGES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE FUNCTIONS IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE IMAGE REPOSITORIES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE SERVICES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 3: NETWORK RULES AND EXTERNAL ACCESS FOR NOTEBOOKS\n-- ============================================================================\n\n-- Create network rule to allow all external access (required for notebooks)\nCREATE OR REPLACE NETWORK RULE ALLOW_ALL_RULE\n    MODE = EGRESS\n    TYPE = HOST_PORT\n    VALUE_LIST = ('0.0.0.0:443', '0.0.0.0:80');\n\n-- Switch back to ACCOUNTADMIN to create integration\nUSE ROLE ACCOUNTADMIN;\n\n-- Create external access integration (requires ACCOUNTADMIN)\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION ALLOW_ALL_INTEGRATION\n    ALLOWED_NETWORK_RULES = (FEATURE_STORE_DEMO.TAXI_FEATURES.ALLOW_ALL_RULE)\n    ENABLED = TRUE;\n\n-- Grant usage on the external access integration to FS_DEMO_ROLE\nGRANT USAGE ON INTEGRATION ALLOW_ALL_INTEGRATION TO ROLE FS_DEMO_ROLE;\n\n-- Switch back to FS_DEMO_ROLE\nUSE ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 4: COMPUTE POOL FOR MODEL DEPLOYMENT\n-- ============================================================================\n\n-- Create compute pool for SPCS model serving\nCREATE COMPUTE POOL IF NOT EXISTS trip_eta_prediction_pool\n    MIN_NODES = 3\n    MAX_NODES = 3\n    INSTANCE_FAMILY = 'CPU_X64_L'\n    AUTO_RESUME = TRUE\n    COMMENT = 'Compute pool for taxi ETA prediction service';\n\n-- Grant usage on compute pool\nGRANT USAGE ON COMPUTE POOL trip_eta_prediction_pool TO ROLE FS_DEMO_ROLE;\nGRANT OPERATE ON COMPUTE POOL trip_eta_prediction_pool TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SETUP COMPLETE\n-- ============================================================================\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will create:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA dedicated role: \u003Ccode\u003EFS_DEMO_ROLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA warehouse: \u003Ccode\u003EFS_DEMO_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA database: \u003Ccode\u003EFEATURE_STORE_DEMO\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA schema: \u003Ccode\u003ETAXI_FEATURES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA stage: \u003Ccode\u003EFS_DEMO_ASSETS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ENetwork rule and external access integration for notebooks\u003C/li\u003E\u003Cli\u003ECompute pool for SPCS model deployment\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe setup script automatically grants the \u003Ccode\u003EFS_DEMO_ROLE\u003C/code\u003E to your current user.\u003C/p\u003E\n","\u003Ch3\u003EUpload and Open Notebook\u003C/h3\u003E\n","\u003Cp\u003ENow that your environment is set up, import the guide notebook to Snowflake.\u003C/p\u003E\n","\u003Ch3\u003ERun the Notebook\u003C/h3\u003E\n","\u003Cp\u003EDownload the notebook file to your local machine:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick this link: \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake/blob/main/notebooks/0_start_here.ipynb\"\u003E0_start_here.ipynb\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EOn the GitHub page, click the \u003Cstrong\u003EDownload raw file\u003C/strong\u003E button (download icon in the top right of the file preview)\u003C/li\u003E\u003Cli\u003ESave the \u003Ccode\u003E.ipynb\u003C/code\u003E file to your computer\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow import the notebook into Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EChange role to \u003Ccode\u003EFS_DEMO_ROLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003ENotebooks\u003C/strong\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EImport .ipynb\u003C/strong\u003E from the \u003Cstrong\u003E+ Notebook\u003C/strong\u003E dropdown\u003C/li\u003E\u003Cli\u003ESelect the downloaded \u003Ccode\u003E0_start_here.ipynb\u003C/code\u003E file from your computer\u003C/li\u003E\u003Cli\u003ECreate a new notebook with the following settings:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENotebook Location\u003C/strong\u003E: \u003Ccode\u003EFEATURE_STORE_DEMO\u003C/code\u003E, \u003Ccode\u003ETAXI_FEATURES\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERun On\u003C/strong\u003E: Container\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E: \u003Ccode\u003EFS_DEMO_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompute Pool\u003C/strong\u003E: \u003Ccode\u003Etrip_eta_prediction_pool\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe notebook will open and be ready to run.\u003C/p\u003E\n","\u003Ch3\u003EAdd External Access to Notebook\u003C/h3\u003E\n","\u003Cp\u003EAfter creating the notebook, you need to configure external access to allow the notebook to install Python packages and access external resources required by Snowflake ML libraries.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen the notebook in Snowsight\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003Ethree dots menu\u003C/strong\u003E (⋮) in the top right\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ENotebook settings\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUnder \u003Cstrong\u003EExternal Access Integrations\u003C/strong\u003E, click \u003Cstrong\u003E+ External Access Integration\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Ccode\u003EALLOW_ALL_INTEGRATION\u003C/code\u003E from the dropdown\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESave\u003C/strong\u003E to apply changes\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nThe \u003Ccode\u003EALLOW_ALL_INTEGRATION\u003C/code\u003E external access integration was created by the setup script and allows the notebook to install Python packages and access external APIs required by Snowflake ML libraries.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ELoad Sample Data\u003C/h3\u003E\n","\u003Cp\u003EThe notebook uses NYC taxi trip data to demonstrate the Online Feature Store. The data is loaded automatically using Snowflake ML's example helper.\u003C/p\u003E\n","\u003Ch3\u003EWhat's in the Dataset\u003C/h3\u003E\n","\u003Cp\u003EThe NYC Yellow Taxi dataset contains:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPickup and dropoff location IDs\u003C/strong\u003E: Geographic areas in NYC\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETimestamps\u003C/strong\u003E: When trips started and ended\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETrip metrics\u003C/strong\u003E: Distance, duration, fare amounts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdditional features\u003C/strong\u003E: Vendor ID, passenger count, payment type\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELoading the Data\u003C/h3\u003E\n","\u003Cp\u003ERun the data loading cells in the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.feature_store.examples.example_helper import ExampleHelper\n\nexample_helper = ExampleHelper(session, TAXI_DB, TAXI_SCHEMA)\nsource_tables = example_helper.load_example('new_york_taxi_features')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates the \u003Ccode\u003ENYC_YELLOW_TRIPS\u003C/code\u003E table with sample taxi trip data ready for feature engineering.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild Feature Store\u003C/h2\u003E\n","\u003Cp\u003EThis section covers initializing the feature store, registering entities, and engineering features for the taxi trip prediction model.\u003C/p\u003E\n","\u003Ch3\u003ECreate Feature Store Instance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.feature_store import CreationMode, FeatureStore\n\nfs = FeatureStore(\n    session=session,\n    database=TAXI_DB,\n    name=TAXI_SCHEMA,\n    default_warehouse=session.get_current_warehouse(),\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Feature Store will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStore feature definitions and metadata\u003C/li\u003E\u003Cli\u003EManage feature versioning\u003C/li\u003E\u003Cli\u003EHandle data synchronization between offline and online stores\u003C/li\u003E\u003Cli\u003ETrack feature lineage\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERegister Entities\u003C/h3\u003E\n","\u003Cp\u003EEntities represent the keys used to join features. For taxi trip prediction, we define entities for routes and pickup times.\u003C/p\u003E\n","\u003Ch3\u003EDefine Route Entity\u003C/h3\u003E\n","\u003Cp\u003EThe route entity represents a trip from one location to another:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.feature_store.entity import Entity\n\nroute_entity = Entity(\n    name="route",\n    join_keys=["PULOCATIONID", "DOLOCATIONID"],\n    desc="A taxi route defined by pickup and dropoff location IDs"\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDefine Pickup Time Entity\u003C/h3\u003E\n","\u003Cp\u003EThe pickup time entity captures temporal patterns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epickup_time_entity = Entity(\n    name="pickup_time",\n    join_keys=["PICKUP_HOUR", "PICKUP_DAY_OF_WEEK"],\n    desc="Pickup time bucketed by hour and day of week"\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERegister Entities\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efs.register_entity(route_entity)\nfs.register_entity(pickup_time_entity)\n\n# View registered entities\nfs.list_entities().show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEngineer Features\u003C/h3\u003E\n","\u003Cp\u003ECreate meaningful features from raw taxi trip data to improve model predictions.\u003C/p\u003E\n","\u003Ch3\u003ETime-Based Features\u003C/h3\u003E\n","\u003Cp\u003EExtract temporal patterns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf = df.with_columns([\n    hour(col("TPEP_PICKUP_DATETIME")).alias("PICKUP_HOUR"),\n    dayofweek(col("TPEP_PICKUP_DATETIME")).alias("PICKUP_DAY_OF_WEEK"),\n    month(col("TPEP_PICKUP_DATETIME")).alias("PICKUP_MONTH"),\n])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDerived Features\u003C/h3\u003E\n","\u003Cp\u003ECalculate trip characteristics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf = df.with_column(\n    "SPEED_MPH",\n    (col("TRIP_DISTANCE") / nullifzero(col("TRIP_DURATION_MIN")) * 60)\n)\n\ndf = df.with_column(\n    "IS_RUSH_HOUR",\n    when((col("PICKUP_HOUR") >= 7) & (col("PICKUP_HOUR") <= 9), 1)\n    .when((col("PICKUP_HOUR") >= 16) & (col("PICKUP_HOUR") <= 18), 1)\n    .otherwise(0)\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAggregate Features\u003C/h3\u003E\n","\u003Cp\u003ECompute route-level statistics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eroute_stats = df.group_by("PULOCATIONID", "DOLOCATIONID").agg([\n    avg("TRIP_DURATION_MIN").alias("AVG_ETA_ROUTE"),\n    avg("TRIP_DISTANCE").alias("AVG_DISTANCE_ROUTE"),\n    avg("SPEED_MPH").alias("AVG_SPEED_ROUTE")\n])\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnable Online Feature Serving\u003C/h2\u003E\n","\u003Cp\u003EThis section covers creating feature views with online serving enabled and monitoring the refresh process.\u003C/p\u003E\n","\u003Ch3\u003EDefine Feature View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.feature_store import feature_view\n\nroute_fv = feature_view.FeatureView(\n    name="nyc_taxi_trip_fv",\n    entities=[route_entity, pickup_time_entity],\n    feature_df=feature_df,\n    timestamp_col="TPEP_PICKUP_DATETIME",\n    refresh_freq="60s",\n    desc="Trip-based features for taxi ETA prediction",\n    online_config=feature_view.OnlineConfig(\n        enable=True, \n        target_lag="10s"\n    ),\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EKey Configuration Parameters\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Erefresh_freq\u003C/strong\u003E: How often to refresh the offline feature table (Dynamic Table)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Etarget_lag\u003C/strong\u003E: Maximum acceptable lag for online features\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eonline_config.enable\u003C/strong\u003E: Enables online serving for low-latency lookups\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERegister Feature View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eregistered_route_fv = fs.register_feature_view(route_fv, "v1", overwrite=True)\n\nprint("Registered feature view:", registered_route_fv.name)\nprint("Online feature table:", registered_route_fv.fully_qualified_online_table_name())\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe online feature table will automatically sync data from the offline store based on the target lag setting.\u003C/p\u003E\n","\u003Ch3\u003EMonitor Online Feature Refresh\u003C/h3\u003E\n","\u003Cp\u003ECheck that the online feature table is refreshing properly.\u003C/p\u003E\n","\u003Ch3\u003EView Refresh History\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efs.get_refresh_history(\n    registered_route_fv, \n    store_type=feature_view.StoreType.ONLINE\n).show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis shows:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERefresh timestamps\u003C/li\u003E\u003Cli\u003ENumber of rows updated\u003C/li\u003E\u003Cli\u003ERefresh status (success/failure)\u003C/li\u003E\u003Cli\u003EAny error messages\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERead from Online Store\u003C/h3\u003E\n","\u003Cp\u003EOnce refreshed, you can read from the online feature store:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eonline_df = fs.read_feature_view(\n    registered_route_fv,\n    store_type=feature_view.StoreType.ONLINE,\n)\nonline_df.show()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nThe first refresh may take 10-30 seconds. If you see "not refreshed yet" error, wait a moment and re-run the cell.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETrain ML Model\u003C/h2\u003E\n","\u003Cp\u003ETrain an XGBoost regression model to predict taxi trip durations using features from the feature store.\u003C/p\u003E\n","\u003Ch3\u003ECreate Train/Test Split\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrain_spine_df, test_spine_df = df.select(spine_cols).random_split([0.85, 0.15], seed=42)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGenerate Training Dataset\u003C/h3\u003E\n","\u003Cp\u003EUse the feature store to automatically join features:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrain_df = fs.generate_training_set(\n    spine_df=train_spine_df,\n    features=[registered_route_fv],\n    spine_timestamp_col="TPEP_PICKUP_DATETIME",\n    include_feature_view_timestamp_col=False\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETrain XGBoost Model\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.modeling.xgboost import XGBRegressor\n\nregressor = XGBRegressor(\n    input_cols=feature_columns,\n    label_cols=["ETA_MINUTES"],\n    output_cols=["predicted_eta"]\n)\nregressor.fit(train_df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEvaluate Model\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.metrics import mean_squared_error, r2_score\n\npredictions = regressor.predict(test_df)\npredictions_pd = predictions.to_pandas()\n\nmse = mean_squared_error(predictions_pd["ETA_MINUTES"], predictions_pd["predicted_eta"])\nr2 = r2_score(predictions_pd["ETA_MINUTES"], predictions_pd["predicted_eta"])\n\nprint(f"Test MSE: {mse:.2f}")\nprint(f"Test R²: {r2:.2f}")\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMake Real-Time Predictions\u003C/h2\u003E\n","\u003Cp\u003EUse online features for low-latency, real-time predictions.\u003C/p\u003E\n","\u003Ch3\u003EDefine Prediction Function\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef predict_trip_duration(pu_location_id, do_location_id, pickup_hour, pickup_day_of_week):\n    trip = [[pu_location_id, do_location_id, pickup_hour, pickup_day_of_week]]\n    \n    # Fetch latest features from online store\n    features_df = fs.read_feature_view(\n        registered_route_fv,\n        keys=trip,\n        store_type=feature_view.StoreType.ONLINE\n    )\n    \n    features_pd = features_df.to_pandas()\n    if features_pd.empty:\n        print("No online features found")\n        return None\n    \n    return regressor.predict(features_pd)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMake a Prediction\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Predict trip from location 141 to 236 at 8am on Sunday\nprediction = predict_trip_duration(141, 236, 8, 0)\nprint(f"Predicted trip duration: {prediction['predicted_eta'][0]:.1f} minutes")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhy Use Online Features?\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELow latency\u003C/strong\u003E: Point lookups by key (milliseconds)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAlways fresh\u003C/strong\u003E: Automatic background refresh\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EScalable\u003C/strong\u003E: Built on Snowflake's elastic infrastructure\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConsistent\u003C/strong\u003E: Same features for training and serving\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERegister Model (Optional)\u003C/h3\u003E\n","\u003Cp\u003ERegister your trained model in Snowflake Model Registry for versioning and deployment.\u003C/p\u003E\n","\u003Ch4\u003ELog Model\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.registry import Registry\n\nregistry = Registry(session=session)\nmodel_name = "NYC_TAXI_ETA_XGB"\n\nmv = registry.log_model(\n    model=regressor,\n    model_name=model_name,\n    comment="Predict NYC taxi trip durations using Feature Store",\n    metrics={"test_mse": float(mse), "test_r2": float(r2)},\n    version_name="v1",\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EView Registered Models\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eregistry.show_models()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Model Registry provides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVersion control for models\u003C/li\u003E\u003Cli\u003ELineage tracking\u003C/li\u003E\u003Cli\u003EDeployment management\u003C/li\u003E\u003Cli\u003EPerformance metrics storage\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDeploy Model to SPCS (Optional)\u003C/h3\u003E\n","\u003Cp\u003EDeploy your model as a containerized service for scalable inference.\u003C/p\u003E\n","\u003Ch4\u003EPrerequisites\u003C/h4\u003E\n","\u003Cp\u003EEnsure the compute pool was created during setup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW COMPUTE POOLS LIKE 'trip_eta_prediction_pool';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ECreate Service\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Elatest_model = registry.get_model(model_name).version("v1")\nservice_name = "NYC_TAXI_ETA_V1"\n\nlatest_model.create_service(\n    service_name=service_name,\n    service_compute_pool="trip_eta_prediction_pool",\n    ingress_enabled=True\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EMake Predictions via Service\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Espcs_prediction = latest_model.run(\n    features_pd, \n    function_name='predict', \n    service_name=service_name\n)\nprint("Prediction from SPCS:", spcs_prediction)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up Guide Resources\u003C/h2\u003E\n","\u003Cp\u003EWhen you're finished with the guide, you can remove all created resources to avoid incurring costs.\u003C/p\u003E\n","\u003Ch3\u003ERun the Teardown Script\u003C/h3\u003E\n","\u003Cp\u003EDownload the teardown script to your local machine:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick this link: \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake/blob/main/scripts/teardown.sql\"\u003Eteardown.sql\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EOn the GitHub page, click the \u003Cstrong\u003EDownload raw file\u003C/strong\u003E button (download icon in the top right of the file preview)\u003C/li\u003E\u003Cli\u003ESave the \u003Ccode\u003E.sql\u003C/code\u003E file to your computer\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow run the teardown script in Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen Snowflake and navigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003EWorkspaces\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECreate a new SQL file\u003C/li\u003E\u003Cli\u003EOpen the downloaded \u003Ccode\u003Eteardown.sql\u003C/code\u003E file and copy all its contents\u003C/li\u003E\u003Cli\u003EPaste the script into your Snowflake SQL file\u003C/li\u003E\u003Cli\u003ERun the entire script as \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe teardown script will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStop all running services\u003C/li\u003E\u003Cli\u003EDrop the compute pool\u003C/li\u003E\u003Cli\u003EDrop the external access integration\u003C/li\u003E\u003Cli\u003EDrop the database (including all tables, views, and dynamic tables)\u003C/li\u003E\u003Cli\u003EDrop the warehouse\u003C/li\u003E\u003Cli\u003ERevoke all account-level permissions\u003C/li\u003E\u003Cli\u003EDrop the \u003Ccode\u003EFS_DEMO_ROLE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE:\nThis will permanently delete all data and resources created during the guide.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built an end-to-end ML workflow using Snowflake Online Feature Store.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up and configure the Snowflake Feature Store\u003C/li\u003E\u003Cli\u003EHow to register entities and create feature views\u003C/li\u003E\u003Cli\u003EHow to enable online serving for real-time inference\u003C/li\u003E\u003Cli\u003EHow to engineer features for machine learning\u003C/li\u003E\u003Cli\u003EHow to train models using feature store data\u003C/li\u003E\u003Cli\u003EHow to make low-latency predictions with online features\u003C/li\u003E\u003Cli\u003E(Optional) How to deploy models to Snowpark Container Services\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EOnline Feature Store\u003C/strong\u003E provides millisecond-latency feature retrieval\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeature Views\u003C/strong\u003E manage both offline (training) and online (inference) features\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAutomatic Refresh\u003C/strong\u003E keeps online features in sync with minimal lag\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPoint Lookups\u003C/strong\u003E by entity keys enable real-time ML applications\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake\"\u003EGitHub Repository - Complete Code and Notebooks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview\"\u003ESnowflake Feature Store Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-ml/index\"\u003ESnowflake ML Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML Resources\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/best-practices-for-production-ml/\"\u003EBest Practices for Production ML\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/agentic-ml-best-practices-cortex-code/\"\u003EAgentic Machine Learning Best Practices with Snowflake CoCo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/create-and-serve-online-features-python\"\u003EOnline Feature Tables Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart\"\u003EMore ML Quickstarts\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ETry the feature store with your own datasets\u003C/li\u003E\u003Cli\u003EExplore incremental refresh modes for cost optimization\u003C/li\u003E\u003Cli\u003EIntegrate online features into production applications\u003C/li\u003E\u003Cli\u003EExperiment with different ML models and feature combinations\u003C/li\u003E\u003C/ul\u003E"],"description":"Build real-time ML predictions using Snowflake Online Feature Store for low-latency feature serving","title":"Introduction to Online Feature Store in Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nThe Snowflake Online Feature Store provides low-latency, key-based feature retrieval for real-time ML inference. This guide demonstrates how to build an end-to-end machine learning workflow using the Online Feature Store to predict taxi trip durations in New York City.\n\nYou'll learn how to register entities and feature views, perform feature engineering, and use both online and offline stores for real-time inference.\n\n### Prerequisites\n- A Snowflake account (non-trial) in AWS or Azure commercial regions\n- Basic knowledge of Python and SQL\n- Familiarity with machine learning concepts\n- ACCOUNTADMIN access or equivalent permissions\n\n### What You'll Learn\n- How to set up the Snowflake Feature Store\n- How to register entities and create feature views\n- How to enable online serving for low-latency inference\n- How to train an XGBoost model using feature store data\n- How to make real-time predictions using online features\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\n- Basic understanding of Snowpark and Snowflake ML\n\n### What You'll Build\n- A complete feature store for taxi trip prediction\n- Online feature views for real-time inference\n- An XGBoost regression model\n- A real-time prediction system using online features\n\n\u003C!-- ------------------------ --\u003E\n## Setup and Data Preparation\n\nThis section covers environment setup, notebook upload, and loading the sample dataset for the feature store guide.\n\n### Run the Setup Script\n\n1. Open Snowflake and navigate to \u003Ca href=\"https://app.snowflake.com/_deeplink/#/workspaces?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=intro-to-online-feature-store-in-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003E**Projects** \u003E **Workspaces**\u003C/a\u003E\n2. Create a new SQL file\n3. Copy and paste the following setup script\n4. Run the entire script as **ACCOUNTADMIN**\n\n```sql\n-- ============================================================================\n-- Snowflake Setup Script for Online Feature Store Demo\n-- ============================================================================\n\nUSE ROLE ACCOUNTADMIN;\n\nSET USERNAME = (SELECT CURRENT_USER());\nSELECT $USERNAME;\n\n-- Set query tag for tracking\nALTER SESSION SET QUERY_TAG = '{\"origin\":\"sf_sit-is\", \"name\":\"sfguide_intro_to_online_feature_store\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"sql\"}}';\n\n-- ============================================================================\n-- SECTION 1: CREATE ROLE AND GRANT ACCOUNT-LEVEL PERMISSIONS\n-- ============================================================================\n\n-- Create role for Feature Store operations and grant to current user\nCREATE OR REPLACE ROLE FS_DEMO_ROLE;\nGRANT ROLE FS_DEMO_ROLE TO USER identifier($USERNAME);\n\n-- Grant account-level permissions\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT CREATE WAREHOUSE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT CREATE COMPUTE POOL ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT IMPORT SHARE ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE FS_DEMO_ROLE;\nGRANT EXECUTE MANAGED TASK ON ACCOUNT TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 2: SWITCH TO ROLE AND CREATE RESOURCES\n-- ============================================================================\n\nUSE ROLE FS_DEMO_ROLE;\n\n-- Create warehouse\nCREATE OR REPLACE WAREHOUSE FS_DEMO_WH\n    WAREHOUSE_SIZE = 'XSMALL'\n    AUTO_SUSPEND = 300\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'Warehouse for Feature Store demo';\n\n-- Create database and schema\nCREATE OR REPLACE DATABASE FEATURE_STORE_DEMO\n    COMMENT = 'Database for Feature Store with taxi trip prediction';\n\nCREATE OR REPLACE SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES\n    COMMENT = 'Schema for taxi features and online feature store';\n\n-- Use the created resources\nUSE WAREHOUSE FS_DEMO_WH;\nUSE DATABASE FEATURE_STORE_DEMO;\nUSE SCHEMA TAXI_FEATURES;\n\n-- Create stage for model assets with directory enabled\nCREATE OR REPLACE STAGE FS_DEMO_ASSETS\n    DIRECTORY = (ENABLE = TRUE)\n    COMMENT = 'Stage for storing model assets and data files';\n\n-- Grant full privileges on database and schema to the role\nGRANT ALL PRIVILEGES ON DATABASE FEATURE_STORE_DEMO TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON WAREHOUSE FS_DEMO_WH TO ROLE FS_DEMO_ROLE;\n\n-- Grant future privileges to handle dynamically created objects\nGRANT ALL PRIVILEGES ON FUTURE TABLES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE VIEWS IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE DYNAMIC TABLES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE STAGES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE FUNCTIONS IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE IMAGE REPOSITORIES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\nGRANT ALL PRIVILEGES ON FUTURE SERVICES IN SCHEMA FEATURE_STORE_DEMO.TAXI_FEATURES TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 3: NETWORK RULES AND EXTERNAL ACCESS FOR NOTEBOOKS\n-- ============================================================================\n\n-- Create network rule to allow all external access (required for notebooks)\nCREATE OR REPLACE NETWORK RULE ALLOW_ALL_RULE\n    MODE = EGRESS\n    TYPE = HOST_PORT\n    VALUE_LIST = ('0.0.0.0:443', '0.0.0.0:80');\n\n-- Switch back to ACCOUNTADMIN to create integration\nUSE ROLE ACCOUNTADMIN;\n\n-- Create external access integration (requires ACCOUNTADMIN)\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION ALLOW_ALL_INTEGRATION\n    ALLOWED_NETWORK_RULES = (FEATURE_STORE_DEMO.TAXI_FEATURES.ALLOW_ALL_RULE)\n    ENABLED = TRUE;\n\n-- Grant usage on the external access integration to FS_DEMO_ROLE\nGRANT USAGE ON INTEGRATION ALLOW_ALL_INTEGRATION TO ROLE FS_DEMO_ROLE;\n\n-- Switch back to FS_DEMO_ROLE\nUSE ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SECTION 4: COMPUTE POOL FOR MODEL DEPLOYMENT\n-- ============================================================================\n\n-- Create compute pool for SPCS model serving\nCREATE COMPUTE POOL IF NOT EXISTS trip_eta_prediction_pool\n    MIN_NODES = 3\n    MAX_NODES = 3\n    INSTANCE_FAMILY = 'CPU_X64_L'\n    AUTO_RESUME = TRUE\n    COMMENT = 'Compute pool for taxi ETA prediction service';\n\n-- Grant usage on compute pool\nGRANT USAGE ON COMPUTE POOL trip_eta_prediction_pool TO ROLE FS_DEMO_ROLE;\nGRANT OPERATE ON COMPUTE POOL trip_eta_prediction_pool TO ROLE FS_DEMO_ROLE;\n\n-- ============================================================================\n-- SETUP COMPLETE\n-- ============================================================================\n```\n\nThis will create:\n- A dedicated role: `FS_DEMO_ROLE`\n- A warehouse: `FS_DEMO_WH`\n- A database: `FEATURE_STORE_DEMO`\n- A schema: `TAXI_FEATURES`\n- A stage: `FS_DEMO_ASSETS` \n- Network rule and external access integration for notebooks\n- Compute pool for SPCS model deployment\n\nThe setup script automatically grants the `FS_DEMO_ROLE` to your current user.\n\n### Upload and Open Notebook\n\nNow that your environment is set up, import the guide notebook to Snowflake.\n\n### Run the Notebook\n\nDownload the notebook file to your local machine:\n\n1. Click this link: [0_start_here.ipynb](https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake/blob/main/notebooks/0_start_here.ipynb)\n2. On the GitHub page, click the **Download raw file** button (download icon in the top right of the file preview)\n3. Save the `.ipynb` file to your computer\n\nNow import the notebook into Snowflake:\n\n1. Change role to `FS_DEMO_ROLE`\n2. Navigate to **Projects** \u003E **Notebooks** in Snowsight\n3. Click **Import .ipynb** from the **+ Notebook** dropdown\n4. Select the downloaded `0_start_here.ipynb` file from your computer\n5. Create a new notebook with the following settings:\n   - **Notebook Location**: `FEATURE_STORE_DEMO`, `TAXI_FEATURES`\n   - **Run On**: Container\n   - **Warehouse**: `FS_DEMO_WH`\n   - **Compute Pool**: `trip_eta_prediction_pool`\n\nThe notebook will open and be ready to run.\n\n### Add External Access to Notebook\n\nAfter creating the notebook, you need to configure external access to allow the notebook to install Python packages and access external resources required by Snowflake ML libraries.\n\n1. Open the notebook in Snowsight\n2. Click the **three dots menu** (⋮) in the top right\n3. Select **Notebook settings**\n4. Under **External Access Integrations**, click **+ External Access Integration**\n5. Select `ALLOW_ALL_INTEGRATION` from the dropdown\n6. Click **Save** to apply changes\n\n\u003E NOTE:\n\u003E The `ALLOW_ALL_INTEGRATION` external access integration was created by the setup script and allows the notebook to install Python packages and access external APIs required by Snowflake ML libraries.\n\n### Load Sample Data\n\nThe notebook uses NYC taxi trip data to demonstrate the Online Feature Store. The data is loaded automatically using Snowflake ML's example helper.\n\n### What's in the Dataset\n\nThe NYC Yellow Taxi dataset contains:\n- **Pickup and dropoff location IDs**: Geographic areas in NYC\n- **Timestamps**: When trips started and ended\n- **Trip metrics**: Distance, duration, fare amounts\n- **Additional features**: Vendor ID, passenger count, payment type\n\n### Loading the Data\n\nRun the data loading cells in the notebook:\n\n```python\nfrom snowflake.ml.feature_store.examples.example_helper import ExampleHelper\n\nexample_helper = ExampleHelper(session, TAXI_DB, TAXI_SCHEMA)\nsource_tables = example_helper.load_example('new_york_taxi_features')\n```\n\nThis creates the `NYC_YELLOW_TRIPS` table with sample taxi trip data ready for feature engineering.\n\n\u003C!-- ------------------------ --\u003E\n## Build Feature Store\n\nThis section covers initializing the feature store, registering entities, and engineering features for the taxi trip prediction model.\n\n### Create Feature Store Instance\n\n```python\nfrom snowflake.ml.feature_store import CreationMode, FeatureStore\n\nfs = FeatureStore(\n    session=session,\n    database=TAXI_DB,\n    name=TAXI_SCHEMA,\n    default_warehouse=session.get_current_warehouse(),\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n)\n```\n\nThe Feature Store will:\n- Store feature definitions and metadata\n- Manage feature versioning\n- Handle data synchronization between offline and online stores\n- Track feature lineage\n\n### Register Entities\n\nEntities represent the keys used to join features. For taxi trip prediction, we define entities for routes and pickup times.\n\n### Define Route Entity\n\nThe route entity represents a trip from one location to another:\n\n```python\nfrom snowflake.ml.feature_store.entity import Entity\n\nroute_entity = Entity(\n    name=\"route\",\n    join_keys=[\"PULOCATIONID\", \"DOLOCATIONID\"],\n    desc=\"A taxi route defined by pickup and dropoff location IDs\"\n)\n```\n\n### Define Pickup Time Entity\n\nThe pickup time entity captures temporal patterns:\n\n```python\npickup_time_entity = Entity(\n    name=\"pickup_time\",\n    join_keys=[\"PICKUP_HOUR\", \"PICKUP_DAY_OF_WEEK\"],\n    desc=\"Pickup time bucketed by hour and day of week\"\n)\n```\n\n### Register Entities\n\n```python\nfs.register_entity(route_entity)\nfs.register_entity(pickup_time_entity)\n\n# View registered entities\nfs.list_entities().show()\n```\n\n### Engineer Features\n\nCreate meaningful features from raw taxi trip data to improve model predictions.\n\n### Time-Based Features\n\nExtract temporal patterns:\n\n```python\ndf = df.with_columns([\n    hour(col(\"TPEP_PICKUP_DATETIME\")).alias(\"PICKUP_HOUR\"),\n    dayofweek(col(\"TPEP_PICKUP_DATETIME\")).alias(\"PICKUP_DAY_OF_WEEK\"),\n    month(col(\"TPEP_PICKUP_DATETIME\")).alias(\"PICKUP_MONTH\"),\n])\n```\n\n### Derived Features\n\nCalculate trip characteristics:\n\n```python\ndf = df.with_column(\n    \"SPEED_MPH\",\n    (col(\"TRIP_DISTANCE\") / nullifzero(col(\"TRIP_DURATION_MIN\")) * 60)\n)\n\ndf = df.with_column(\n    \"IS_RUSH_HOUR\",\n    when((col(\"PICKUP_HOUR\") \u003E= 7) & (col(\"PICKUP_HOUR\") \u003C= 9), 1)\n    .when((col(\"PICKUP_HOUR\") \u003E= 16) & (col(\"PICKUP_HOUR\") \u003C= 18), 1)\n    .otherwise(0)\n)\n```\n\n### Aggregate Features\n\nCompute route-level statistics:\n\n```python\nroute_stats = df.group_by(\"PULOCATIONID\", \"DOLOCATIONID\").agg([\n    avg(\"TRIP_DURATION_MIN\").alias(\"AVG_ETA_ROUTE\"),\n    avg(\"TRIP_DISTANCE\").alias(\"AVG_DISTANCE_ROUTE\"),\n    avg(\"SPEED_MPH\").alias(\"AVG_SPEED_ROUTE\")\n])\n```\n\n\u003C!-- ------------------------ --\u003E\n## Enable Online Feature Serving\n\nThis section covers creating feature views with online serving enabled and monitoring the refresh process.\n\n### Define Feature View\n\n```python\nfrom snowflake.ml.feature_store import feature_view\n\nroute_fv = feature_view.FeatureView(\n    name=\"nyc_taxi_trip_fv\",\n    entities=[route_entity, pickup_time_entity],\n    feature_df=feature_df,\n    timestamp_col=\"TPEP_PICKUP_DATETIME\",\n    refresh_freq=\"60s\",\n    desc=\"Trip-based features for taxi ETA prediction\",\n    online_config=feature_view.OnlineConfig(\n        enable=True, \n        target_lag=\"10s\"\n    ),\n)\n```\n\n### Key Configuration Parameters\n\n- **refresh_freq**: How often to refresh the offline feature table (Dynamic Table)\n- **target_lag**: Maximum acceptable lag for online features\n- **online_config.enable**: Enables online serving for low-latency lookups\n\n### Register Feature View\n\n```python\nregistered_route_fv = fs.register_feature_view(route_fv, \"v1\", overwrite=True)\n\nprint(\"Registered feature view:\", registered_route_fv.name)\nprint(\"Online feature table:\", registered_route_fv.fully_qualified_online_table_name())\n```\n\nThe online feature table will automatically sync data from the offline store based on the target lag setting.\n\n### Monitor Online Feature Refresh\n\nCheck that the online feature table is refreshing properly.\n\n### View Refresh History\n\n```python\nfs.get_refresh_history(\n    registered_route_fv, \n    store_type=feature_view.StoreType.ONLINE\n).show()\n```\n\nThis shows:\n- Refresh timestamps\n- Number of rows updated\n- Refresh status (success/failure)\n- Any error messages\n\n### Read from Online Store\n\nOnce refreshed, you can read from the online feature store:\n\n```python\nonline_df = fs.read_feature_view(\n    registered_route_fv,\n    store_type=feature_view.StoreType.ONLINE,\n)\nonline_df.show()\n```\n\n\u003E NOTE:\n\u003E The first refresh may take 10-30 seconds. If you see \"not refreshed yet\" error, wait a moment and re-run the cell.\n\n\u003C!-- ------------------------ --\u003E\n## Train ML Model\n\nTrain an XGBoost regression model to predict taxi trip durations using features from the feature store.\n\n### Create Train/Test Split\n\n```python\ntrain_spine_df, test_spine_df = df.select(spine_cols).random_split([0.85, 0.15], seed=42)\n```\n\n### Generate Training Dataset\n\nUse the feature store to automatically join features:\n\n```python\ntrain_df = fs.generate_training_set(\n    spine_df=train_spine_df,\n    features=[registered_route_fv],\n    spine_timestamp_col=\"TPEP_PICKUP_DATETIME\",\n    include_feature_view_timestamp_col=False\n)\n```\n\n### Train XGBoost Model\n\n```python\nfrom snowflake.ml.modeling.xgboost import XGBRegressor\n\nregressor = XGBRegressor(\n    input_cols=feature_columns,\n    label_cols=[\"ETA_MINUTES\"],\n    output_cols=[\"predicted_eta\"]\n)\nregressor.fit(train_df)\n```\n\n### Evaluate Model\n\n```python\nfrom sklearn.metrics import mean_squared_error, r2_score\n\npredictions = regressor.predict(test_df)\npredictions_pd = predictions.to_pandas()\n\nmse = mean_squared_error(predictions_pd[\"ETA_MINUTES\"], predictions_pd[\"predicted_eta\"])\nr2 = r2_score(predictions_pd[\"ETA_MINUTES\"], predictions_pd[\"predicted_eta\"])\n\nprint(f\"Test MSE: {mse:.2f}\")\nprint(f\"Test R²: {r2:.2f}\")\n```\n\n\u003C!-- ------------------------ --\u003E\n## Make Real-Time Predictions\n\nUse online features for low-latency, real-time predictions.\n\n### Define Prediction Function\n\n```python\ndef predict_trip_duration(pu_location_id, do_location_id, pickup_hour, pickup_day_of_week):\n    trip = [[pu_location_id, do_location_id, pickup_hour, pickup_day_of_week]]\n    \n    # Fetch latest features from online store\n    features_df = fs.read_feature_view(\n        registered_route_fv,\n        keys=trip,\n        store_type=feature_view.StoreType.ONLINE\n    )\n    \n    features_pd = features_df.to_pandas()\n    if features_pd.empty:\n        print(\"No online features found\")\n        return None\n    \n    return regressor.predict(features_pd)\n```\n\n### Make a Prediction\n\n```python\n# Predict trip from location 141 to 236 at 8am on Sunday\nprediction = predict_trip_duration(141, 236, 8, 0)\nprint(f\"Predicted trip duration: {prediction['predicted_eta'][0]:.1f} minutes\")\n```\n\n### Why Use Online Features?\n\n- **Low latency**: Point lookups by key (milliseconds)\n- **Always fresh**: Automatic background refresh\n- **Scalable**: Built on Snowflake's elastic infrastructure\n- **Consistent**: Same features for training and serving\n\n### Register Model (Optional)\n\nRegister your trained model in Snowflake Model Registry for versioning and deployment.\n\n#### Log Model\n\n```python\nfrom snowflake.ml.registry import Registry\n\nregistry = Registry(session=session)\nmodel_name = \"NYC_TAXI_ETA_XGB\"\n\nmv = registry.log_model(\n    model=regressor,\n    model_name=model_name,\n    comment=\"Predict NYC taxi trip durations using Feature Store\",\n    metrics={\"test_mse\": float(mse), \"test_r2\": float(r2)},\n    version_name=\"v1\",\n)\n```\n\n#### View Registered Models\n\n```python\nregistry.show_models()\n```\n\nThe Model Registry provides:\n- Version control for models\n- Lineage tracking\n- Deployment management\n- Performance metrics storage\n\n### Deploy Model to SPCS (Optional)\n\nDeploy your model as a containerized service for scalable inference.\n\n#### Prerequisites\n\nEnsure the compute pool was created during setup:\n\n```sql\nSHOW COMPUTE POOLS LIKE 'trip_eta_prediction_pool';\n```\n\n#### Create Service\n\n```python\nlatest_model = registry.get_model(model_name).version(\"v1\")\nservice_name = \"NYC_TAXI_ETA_V1\"\n\nlatest_model.create_service(\n    service_name=service_name,\n    service_compute_pool=\"trip_eta_prediction_pool\",\n    ingress_enabled=True\n)\n```\n\n#### Make Predictions via Service\n\n```python\nspcs_prediction = latest_model.run(\n    features_pd, \n    function_name='predict', \n    service_name=service_name\n)\nprint(\"Prediction from SPCS:\", spcs_prediction)\n```\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up Guide Resources\n\nWhen you're finished with the guide, you can remove all created resources to avoid incurring costs.\n\n### Run the Teardown Script\n\nDownload the teardown script to your local machine:\n\n1. Click this link: [teardown.sql](https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake/blob/main/scripts/teardown.sql)\n2. On the GitHub page, click the **Download raw file** button (download icon in the top right of the file preview)\n3. Save the `.sql` file to your computer\n\nNow run the teardown script in Snowflake:\n\n1. Open Snowflake and navigate to **Projects** \u003E **Workspaces**\n2. Create a new SQL file\n3. Open the downloaded `teardown.sql` file and copy all its contents\n4. Paste the script into your Snowflake SQL file\n5. Run the entire script as **ACCOUNTADMIN**\n\nThe teardown script will:\n- Stop all running services\n- Drop the compute pool\n- Drop the external access integration\n- Drop the database (including all tables, views, and dynamic tables)\n- Drop the warehouse\n- Revoke all account-level permissions\n- Drop the `FS_DEMO_ROLE`\n\n\u003E NOTE:\n\u003E This will permanently delete all data and resources created during the guide.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've successfully built an end-to-end ML workflow using Snowflake Online Feature Store.\n\n### What You Learned\n- How to set up and configure the Snowflake Feature Store\n- How to register entities and create feature views\n- How to enable online serving for real-time inference\n- How to engineer features for machine learning\n- How to train models using feature store data\n- How to make low-latency predictions with online features\n- (Optional) How to deploy models to Snowpark Container Services\n\n### Key Takeaways\n\n- **Online Feature Store** provides millisecond-latency feature retrieval\n- **Feature Views** manage both offline (training) and online (inference) features\n- **Automatic Refresh** keeps online features in sync with minimal lag\n- **Point Lookups** by entity keys enable real-time ML applications\n\n### Related Resources\n\n- [GitHub Repository - Complete Code and Notebooks](https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake)\n- [Snowflake Feature Store Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview)\n- [Snowflake ML Docs](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index)\n- [Snowflake ML Resources](http://www.snowflake.com/ml)\n- [Best Practices for Production ML](https://www.snowflake.com/en/engineering-blog/best-practices-for-production-ml/)\n- [Agentic Machine Learning Best Practices with Snowflake CoCo](https://www.snowflake.com/en/engineering-blog/agentic-ml-best-practices-cortex-code/)\n- [Online Feature Tables Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/create-and-serve-online-features-python)\n- [More ML Quickstarts](https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart)\n\n### Next Steps\n\n- Try the feature store with your own datasets\n- Explore incremental refresh modes for cost optimization\n- Integrate online features into production applications\n- Experiment with different ML models and feature combinations\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-916eb85cfc","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-402bef45d4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-eee62f007e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6f4e830b50","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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