The list below contains the functionality that contributors are planning to develop for Feast

• Items below that are in development (or planned for development) will be indicated in parentheses.

• We welcome contribution to all items in the roadmap!

• Want to influence our roadmap and prioritization? Submit your feedback to this form.

• Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using Calendly.

• Data Sources

  • Snowflake source

  • Redshift source

  • BigQuery source

  • Parquet file source

  • Synapse source (community plugin)

  • Hive (community plugin)

  • Postgres (community plugin)

  • Spark (community plugin)

  • Kafka / Kinesis sources (via push support into the online store)

  • HTTP source

• Offline Stores

  • Snowflake

  • Redshift

  • BigQuery

  • Synapse (community plugin)

  • Hive (community plugin)

  • Postgres (community plugin)

  • Trino (community plugin)

  • Spark (community plugin)

  • In-memory / Pandas

  • Custom offline store support

• Online Stores

  • DynamoDB

  • Redis

  • Datastore

  • SQLite

  • Azure Cache for Redis (community plugin)

  • Postgres (community plugin)

  • Custom online store support

  • Bigtable (in progress)

  • Cassandra

• Streaming

  • Custom streaming ingestion job support

  • Push based streaming data ingestion

  • Streaming ingestion on AWS

  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Alpha release. See RFC)

  • Batch transformation (In progress. See RFC)

  • Streaming transformation

• Deployments

  • AWS Lambda (Alpha release. See RFC)

  • Kubernetes (See guide)

  • Cloud Run

  • KNative

• Feature Serving

  • Python Client

  • REST Feature Server (Python) (Alpha release. See RFC)

  • gRPC Feature Server (Java) (See #1497)

  • Push API

  • Java Client

  • Go Client

  • Delete API

  • Feature Logging (for training)

• Data Quality Management (See RFC)

  • Data profiling and validation (Great Expectations)

  • Training-serving skew detection (in progress)

  • Metric production

  • Drift detection

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • Amundsen integration (see Feast extractor)

  • Feast Web UI (alpha)

  • REST API for browsing feature registry

  • Feature versioning

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

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:

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 .

  • People interested in the Feast community newsletter should join feast-announce by clicking .

• : Includes community calls and design meetings.

• : 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).

Community Calls

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

Frequency (every 2 weeks)

• Tuesday 10:00 am to 10:30 am PST

Links