What is Feast?

Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).

Problems Feast Solves

Models need consistent access to data: Machine Learning (ML) systems built on traditional data infrastructure are often coupled to databases, object stores, streams, and files. A result of this coupling, however, is that any change in data infrastructure may break dependent ML systems. Another challenge is that dual implementations of data retrieval for training and serving can lead to inconsistencies in data, which in turn can lead to training-serving skew.

Feast decouples your models from your data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval. Feast also provides a consistent means of referencing feature data for retrieval, and therefore ensures that models remain portable when moving from training to serving.

Deploying new features into production is difficult: Many ML teams consist of members with different objectives. Data scientists, for example, aim to deploy features into production as soon as possible, while engineers want to ensure that production systems remain stable. These differing objectives can create an organizational friction that slows time-to-market for new features.

Feast addresses this friction by providing both a centralized registry to which data scientists can publish features and a battle-hardened serving layer. Together, these enable non-engineering teams to ship features into production with minimal oversight.

Models need point-in-time correct data: ML models in production require a view of data consistent with the one on which they are trained, otherwise the accuracy of these models could be compromised. Despite this need, many data science projects suffer from inconsistencies introduced by future feature values being leaked to models during training.

Feast solves the challenge of data leakage by providing point-in-time correct feature retrieval when exporting feature datasets for model training.

Features aren't reused across projects: Different teams within an organization are often unable to reuse features across projects. The siloed nature of development and the monolithic design of end-to-end ML systems contribute to duplication of feature creation and usage across teams and projects.

Feast addresses this problem by introducing feature reuse through a centralized registry. This registry enables multiple teams working on different projects not only to contribute features, but also to reuse these same features. With Feast, data scientists can start new ML projects by selecting previously engineered features from a centralized registry, and are no longer required to develop new features for each project.

Problems Feast does not yet solve

Feature engineering: We aim for Feast to support light-weight feature engineering as part of our API.

Feature discovery: We also aim for Feast to include a first-class user interface for exploring and discovering entities and features.

‌Feature validation: We additionally aim for Feast to improve support for statistics generation of feature data and subsequent validation of these statistics. Current support is limited.

What Feast is not

ETL or ELT system: Feast is not (and does not plan to become) a general purpose data transformation or pipelining system. Feast plans to include a light-weight feature engineering toolkit, but we encourage teams to integrate Feast with upstream ETL/ELT systems that are specialized in transformation.

Data warehouse: Feast is not a replacement for your data warehouse or the source of truth for all transformed data in your organization. Rather, Feast is a light-weight downstream layer that can serve data from an existing data warehouse (or other data sources) to models in production.

Data catalog: Feast is not a general purpose data catalog for your organization. Feast is purely focused on cataloging features for use in ML pipelines or systems, and only to the extent of facilitating the reuse of features.

How can I get started?

Explore the following resources to get started with Feast:

• Quickstart is the fastest way to get started with Feast

• Concepts describes all important Feast API concepts

• Architecture describes Feast's overall architecture.

• Tutorials shows full examples of using Feast in machine learning applications.

• Running Feast with GCP/AWS provides a more in-depth guide to using Feast.

• Reference contains detailed API and design documents.

• Contributing contains resources for anyone who wants to contribute to Feast.

The data source refers to raw underlying data (e.g. a table in BigQuery).

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or when materializing features into an online store.

Below is an example data source with a single entity (driver) and two features (trips_today, and rating).

In this tutorial we will

1. Deploy a local feature store with a Parquet file offline store and Sqlite online store.

2. Build a training dataset using our time series features from our Parquet files.

3. Materialize feature values from the offline store into the online store.

4. Read the latest features from the online store for inference.

You can run this tutorial in Google Colab or run it on your localhost, following the guided steps below.

Overview

In this tutorial, we use feature stores to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:

1. Training-serving skew and complex data joins: Feature values often exist across multiple tables. Joining these datasets can be complicated, slow, and error-prone.

   • Feast joins these tables with battle-tested logic that ensures point-in-time correctness so future feature values do not leak to models.

   • *Upcoming: Feast alerts users to offline / online skew with data quality monitoring.

2. Online feature availability: At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources.

   • Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently available and freshly computed at inference time.

3. Feature reusability and model versioning: Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.

   • Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via feature services).

   • Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.

Step 1: Install Feast

Install the Feast SDK and CLI using pip:

Step 2: Create a feature repository

Bootstrap a new feature repository using feast init from the command line.

Let's take a look at the resulting demo repo itself. It breaks down into

• data/ contains raw demo parquet data

• example.py contains demo feature definitions

• feature_store.yaml contains a demo setup configuring where data sources are

The key line defining the overall architecture of the feature store is the provider. This defines where the raw data exists (for generating training data & feature values for serving), and where to materialize feature values to in the online store (for serving).

Valid values for provider in feature_store.yaml are:

• local: use file source / SQLite

• gcp: use BigQuery / Google Cloud Datastore

• aws: use Redshift / DynamoDB

A custom setup (e.g. using the built-in support for Redis) can be made by following Creating a custom provider

Step 3: Register feature definitions and deploy your feature store

The apply command scans python files in the current directory for feature view/entity definitions, registers the objects, and deploys infrastructure. In this example, it reads example.py (shown again below for convenience) and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by using the local provider in feature_store.yaml.

Step 4: Generating training data

To train a model, we need features and labels. Often, this label data is stored separately (e.g. you have one table storing user survey results and another set of tables with feature values).

The user can query that table of labels with timestamps and pass that into Feast as an entity dataframe for training data generation. In many cases, Feast will also intelligently join relevant tables to create the relevant feature vectors.

• Note that we include timestamps because want the features for the same driver at various timestamps to be used in a model.

Step 5: Load features into your online store

We now serialize the latest values of features since the beginning of time to prepare for serving (note: materialize-incremental serializes all new features since the last materialize call).

Step 6: Fetching feature vectors for inference

At inference time, we need to quickly read the latest feature values for different drivers (which otherwise might have existed only in batch sources) from the online feature store using get_online_features(). These feature vectors can then be fed to the model.

Next steps

Links & Resources

• : Feel free to ask questions or say hello!

• : We have both a user and developer mailing list.

  • Feast users should join group by clicking .

  • Feast developers should join group by clicking .

• : This folder is used as a central repository for all Feast resources. For example:

  • Design proposals in the form of Request for Comments (RFC).

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

• : Find the complete Feast codebase on GitHub.

• : Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

• Slack: Need to speak to a human? Come ask a question in our Slack channel (link above).

• GitHub Issues: Found a bug or need a feature? .

• StackOverflow: Need to ask a question on how to use Feast? We also monitor and respond to .

Community Calls

We have a user and contributor community call every two weeks (Asia & US friendly).

Frequency (alternating times every 2 weeks)

• Tuesday 18:00 pm to 18:30 pm (US, Asia)

• Tuesday 10:00 am to 10:30 am (US, Europe)

Links