{"id":121234,"date":"2026-07-10T12:47:44","date_gmt":"2026-07-10T07:17:44","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=121234"},"modified":"2026-07-10T12:47:46","modified_gmt":"2026-07-10T07:17:46","slug":"feast-feature-store-tutorial","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/feast-feature-store-tutorial\/","title":{"rendered":"Feast Feature Store Tutorial: Key Steps for ML Workflows"},"content":{"rendered":"\n<p>Training a model is the easy part. Keeping its features consistent between training and production is where most ML teams trip up. You build a slick model in a notebook, it performs great, then it falls apart in production because the features it sees in real time don\u2019t match what it learned on. That\u2019s the exact problem Feast was built to solve.&nbsp;<\/p>\n\n\n\n<p>This tutorial walks you through setting up Feast step by step, from installing the SDK to materializing features into an online store, so your models get clean, consistent data every single time they make a prediction.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>Feast is used to manage ML features across training and inference.<\/li>\n\n\n\n<li>It uses a feature repository as the source of truth for feature definitions.<\/li>\n\n\n\n<li>A FeatureView defines a logical group of features from a data source.<\/li>\n\n\n\n<li>An Entity represents the business object linked to features, such as driver, customer, user, or product.<\/li>\n\n\n\n<li>The offline store is used for training data and batch scoring.<\/li>\n\n\n\n<li>The online store is used for real-time feature retrieval during inference.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is Feast Feature Store?<\/strong><\/h2>\n\n\n\n<p>Feast, also called Feature Store, is an operational data system for managing machine learning features across offline training and online serving. It lets ML teams define entities, feature views, data sources, feature services, and feature retrieval logic through Python and configuration files.&nbsp;<\/p>\n\n\n\n<p>Feast stores feature definitions in a feature repository, retrieves historical point-in-time features for model training, and serves the latest features from an online store during real-time inference.&nbsp;<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\">\n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \n  <strong style=\"color: #FFFFFF;\">Feast<\/strong> retrieves historical features using <strong style=\"color: #FFFFFF;\">point-in-time joins<\/strong>, so your training data never accidentally leaks <strong style=\"color: #FFFFFF;\">future information<\/strong>.\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Top Benefits of Feast<\/strong><\/h2>\n\n\n\n<ul>\n<li><strong>Centralizes ML feature definitions:<\/strong> Keeps feature logic, entities, and feature views organized in one repository.<\/li>\n\n\n\n<li><strong>Reduces duplicate feature engineering logic:<\/strong> Avoids rebuilding the same features across different <a href=\"https:\/\/www.guvi.in\/blog\/best-machine-learning-project-ideas\/\" target=\"_blank\" rel=\"noreferrer noopener\">ML projects.<\/a><\/li>\n\n\n\n<li><strong>Supports offline and online feature retrieval:<\/strong> Provides historical features for training and fresh features for inference.<\/li>\n\n\n\n<li><strong>Improves training and serving consistency:<\/strong> Uses shared feature definitions across model development and production serving.<\/li>\n\n\n\n<li><strong>Helps prevent point-in-time data leakage:<\/strong> Retrieves historical features based on event timestamps for accurate training data.<\/li>\n\n\n\n<li><strong>Supports feature reuse through feature services:<\/strong> Groups model-specific features for cleaner experiment and deployment workflows.<\/li>\n\n\n\n<li><strong>Works with Python-based ML workflows:<\/strong> Fits naturally into Python, pandas, and common ML development pipelines.<\/li>\n\n\n\n<li><strong>Fits production AI and <\/strong><a href=\"https:\/\/www.guvi.in\/blog\/what-is-mlops\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>MLOps<\/strong><\/a><strong> pipelines:<\/strong> Supports scalable feature management for real-time and batch ML systems.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Feast Feature Store Tutorial: Key Steps<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Install Feast<\/h3>\n\n\n\n<p>Install the Feast SDK and CLI in your Python environment.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install feast\n<\/code><\/pre>\n\n\n\n<p>The Feast quickstart uses the Python SDK for a local feature store workflow.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Create a Feature Repository<\/h3>\n\n\n\n<p>Create a new Feast project using the CLI.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>feast init my_project\ncd my_project\/feature_repo\n<\/code><\/pre>\n\n\n\n<p>A Feast feature repository usually contains raw demo data, Python feature definition files, feature_store.yaml, and workflow files. The feature repository acts as the declarative source of truth for the feature store.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: Understand feature_store.yaml<\/h3>\n\n\n\n<p>The feature_store.yaml file defines the project, registry, provider, and online store configuration.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>project: my_project\nregistry: data\/registry.db\nprovider: local\nonline_store:\n  type: sqlite\n  path: data\/online_store.db\nentity_key_serialization_version: 3\n<\/code><\/pre>\n\n\n\n<p>In a local setup, Feast can use a file-based registry and SQLite online store. Larger production systems can use cloud or scalable infrastructure depending on the provider and store configuration.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Define an Entity<\/h3>\n\n\n\n<p>An entity represents the object for which features are stored and retrieved. Examples include driver_id, customer_id, user_id, product_id, or merchant_id.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from feast import Entity\n\ndriver = Entity(\n    name=\"driver\",\n    join_keys=&#91;\"driver_id\"],\n)\n<\/code><\/pre>\n\n\n\n<p>In Feast, feature views can map to zero or more entities, and the entity key is used during feature retrieval.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Define a Data Source<\/h3>\n\n\n\n<p>A data source tells Feast where feature data is stored. For a local tutorial, a Parquet file is commonly used.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from feast import FileSource\n\ndriver_stats_source = FileSource(\n    path=\"data\/driver_stats.parquet\",\n    timestamp_field=\"event_timestamp\",\n    created_timestamp_column=\"created\",\n)\n<\/code><\/pre>\n\n\n\n<p>The event timestamp is important because Feast uses timestamps during point-in-time joins. This helps retrieve the correct historical feature values without leaking future information into model training.<\/p>\n\n\n\n<p><em>Build strong Python programming skills to understand ML workflows, feature pipelines, and backend automation better with HCL GUVI\u2019s <\/em><a href=\"https:\/\/www.guvi.in\/courses\/programming\/python-zero-to-hero\/?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=feast-feature-store-tutorial-key-steps-for-ml-workflows\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Python Zero to Hero Course<\/em><\/a><em>. Learn Python fundamentals, problem-solving, scripting logic, and real-world programming concepts through 100% online self-paced learning, globally recognised certification, lifetime content access, dedicated forum support, 4 gamified practice platforms, and a 7-day refund policy.<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: Create a Feature View<\/h3>\n\n\n\n<p>A FeatureView defines a logical group of time-series features from a data source. It includes the feature schema, entity mapping, source, and time-to-live setting.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from datetime import timedelta\nfrom feast import FeatureView, Field\nfrom feast.types import Float32, Int64\n\ndriver_hourly_stats = FeatureView(\n    name=\"driver_hourly_stats\",\n    entities=&#91;driver],\n    ttl=timedelta(days=1),\n    schema=&#91;\n        Field(name=\"conv_rate\", dtype=Float32),\n        Field(name=\"acc_rate\", dtype=Float32),\n        Field(name=\"avg_daily_trips\", dtype=Int64),\n    ],\n    source=driver_stats_source,\n    online=True,\n)\n<\/code><\/pre>\n\n\n\n<p>A FeatureView represents a logical group of feature data as it exists in a source. It usually contains a data source, entities, feature schema, and optional transformation logic.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 7: Create a Feature Service<\/h3>\n\n\n\n<p>A FeatureService groups features needed by a specific model version. This is useful when different models need different feature sets.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from feast import FeatureService\n\ndriver_activity_v1 = FeatureService(\n    name=\"driver_activity_v1\",\n    features=&#91;driver_hourly_stats],\n)\n<\/code><\/pre>\n\n\n\n<p>Feast recommends feature services when models move toward experimentation or serving because they group model-specific features and help track feature sets across model versions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 8: Register Feature Definitions<\/h3>\n\n\n\n<p>Run feast apply from the feature repository.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>feast apply\n<\/code><\/pre>\n\n\n\n<p>The apply command scans Python files in the current directory, registers entities and feature views, and deploys required infrastructure such as online store tables in the configured backend.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 9: Generate Training Data<\/h3>\n\n\n\n<p>Use get_historical_features() to create a training dataset. The entity dataframe must include entity keys and timestamps.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from datetime import datetime\nimport pandas as pd\nfrom feast import FeatureStore\n\nstore = FeatureStore(repo_path=\".\")\n\nentity_df = pd.DataFrame.from_dict({\n    \"driver_id\": &#91;1001, 1002, 1003],\n    \"event_timestamp\": &#91;\n        datetime(2024, 4, 12, 10, 59, 42),\n        datetime(2024, 4, 12, 8, 12, 10),\n        datetime(2024, 4, 12, 16, 40, 26),\n    ],\n})\n\ntraining_df = store.get_historical_features(\n    entity_df=entity_df,\n    features=&#91;\n        \"driver_hourly_stats:conv_rate\",\n        \"driver_hourly_stats:acc_rate\",\n        \"driver_hourly_stats:avg_daily_trips\",\n    ],\n).to_df()\n\nprint(training_df.head())\n<\/code><\/pre>\n\n\n\n<p>Feast uses get_historical_features() to join features onto entity rows for training data or batch scoring. This helps create point-in-time correct datasets from one or more feature views.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 10: Materialize Features into the Online Store<\/h3>\n\n\n\n<p>Load feature values from the offline store into the online store.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>feast materialize 2024-04-07T00:00:00 2024-04-08T00:00:00\n<\/code><\/pre>\n\n\n\n<p>For incremental updates, use:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>feast materialize-incremental 2024-04-08T00:00:00\n<\/code><\/pre>\n\n\n\n<p>Materialization loads feature data into the online store so models can retrieve the latest features during online prediction. The materialize command loads features over a selected time range, while materialize-incremental loads new data since the last materialization run.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 11: Retrieve Online Features for Inference<\/h3>\n\n\n\n<p>Use get_online_features() during real-time model serving.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from pprint import pprint\nfrom feast import FeatureStore\n\nstore = FeatureStore(repo_path=\".\")\n\nfeature_vector = store.get_online_features(\n    features=&#91;\n        \"driver_hourly_stats:conv_rate\",\n        \"driver_hourly_stats:acc_rate\",\n        \"driver_hourly_stats:avg_daily_trips\",\n    ],\n    entity_rows=&#91;\n        {\"driver_id\": 1004},\n        {\"driver_id\": 1005},\n    ],\n).to_dict()\n\npprint(feature_vector)\n<\/code><\/pre>\n\n\n\n<p>Online feature retrieval does not need timestamps because the model usually needs the latest available feature value for each entity key. Feast supports online retrieval through the Python SDK or an optional feature server.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 12: Use Feature Services for Model-Specific Retrieval<\/h3>\n\n\n\n<p>Use the registered feature service when retrieving features for a model version.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from feast import FeatureStore\n\nfeature_store = FeatureStore(repo_path=\".\")\nfeature_service = feature_store.get_feature_service(\"driver_activity_v1\")\n\nfeature_vector = feature_store.get_online_features(\n    features=feature_service,\n    entity_rows=&#91;\n        {\"driver_id\": 1004},\n        {\"driver_id\": 1005},\n    ],\n).to_dict()\n\nprint(feature_vector)\n<\/code><\/pre>\n\n\n\n<p>Feature services help keep feature retrieval aligned with model versions. A single training dataset may include features from multiple feature views, and online retrieval can also pull features from multiple feature views through a feature service.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 13: Explore Features with the Feast UI<\/h3>\n\n\n\n<p>Start the Feast UI.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>feast ui\n<\/code><\/pre>\n\n\n\n<p>The Feast UI helps teams view and explore feature information defined in the project. Feast also provides CLI tooling, <a href=\"https:\/\/www.guvi.in\/blog\/guide-to-python-web-development\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python SDK<\/a> APIs, and an optional feature server for reading and writing feature data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Feast Concepts to Remember<\/strong><\/h2>\n\n\n\n<ol>\n<li><strong>Entity<\/strong><\/li>\n<\/ol>\n\n\n\n<p>An entity is the business object linked to features, such as a driver, user, customer, product, or merchant.<\/p>\n\n\n\n<ol start=\"2\">\n<li><strong>Feature View<\/strong><\/li>\n<\/ol>\n\n\n\n<p>A FeatureView defines a logical group of features from a data source. It includes schema, source, entity mapping, and serving settings.<\/p>\n\n\n\n<ol start=\"3\">\n<li><strong>Offline Store<\/strong><\/li>\n<\/ol>\n\n\n\n<p>The offline store is used for historical feature retrieval, model training, and batch scoring.<\/p>\n\n\n\n<ol start=\"4\">\n<li><strong>Online Store<\/strong><\/li>\n<\/ol>\n\n\n\n<p>The online store serves low-latency features for production inference.<\/p>\n\n\n\n<ol start=\"5\">\n<li><strong>Feature Service<\/strong><\/li>\n<\/ol>\n\n\n\n<p>A FeatureService groups features from one or more feature views for a specific model version.<\/p>\n\n\n\n<ol start=\"6\">\n<li><strong>Materialization<\/strong><\/li>\n<\/ol>\n\n\n\n<p>Materialization moves features from offline storage into the online store for fast inference-time lookup.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Feast is useful for teams that have moved beyond experiments and now need their machine learning models to work reliably in production. It gives data scientists and <a href=\"https:\/\/www.guvi.in\/blog\/how-to-become-a-top-machine-learning-engineer\/\" target=\"_blank\" rel=\"noreferrer noopener\">ML engineers<\/a> a clear way to define features, build training datasets, push fresh features to an online store, and fetch them quickly when a model makes a prediction.\u00a0<\/p>\n\n\n\n<p>A good Feast setup keeps everything connected, from raw data and feature definitions to training, serving, and model-specific feature sets. This helps AI teams reuse features, reduce confusion, and keep training and real-time predictions consistent.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">FAQs<\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1783340338758\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is Feast feature store used for?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Feast is used to define, manage, and serve machine learning features for training, batch scoring, and real-time inference.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783340410206\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Is Feast an online or offline feature store?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Feast supports both. It uses an offline store for historical features and an online store for low-latency inference features.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783340419289\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is materialization in Feast?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Materialization is the process of loading feature values from an offline source into an online store so production models can retrieve them quickly.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Training a model is the easy part. Keeping its features consistent between training and production is where most ML teams trip up. You build a slick model in a notebook, it performs great, then it falls apart in production because the features it sees in real time don\u2019t match what it learned on. That\u2019s the [&hellip;]<\/p>\n","protected":false},"author":60,"featured_media":122553,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"36","authorinfo":{"name":"Vaishali","url":"https:\/\/www.guvi.in\/blog\/author\/vaishali\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/Feast-Feature-Store-Tutorial-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/121234"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/60"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=121234"}],"version-history":[{"count":3,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/121234\/revisions"}],"predecessor-version":[{"id":122555,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/121234\/revisions\/122555"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/122553"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=121234"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=121234"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=121234"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}