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GitHub Repository: Find the complete Feast codebase on GitHub.
Community Governance Doc: See the governance model of Feast, including who the maintainers are and how decisions are made.
Slack: Feel free to ask questions or say hello! This is the main place where maintainers and contributors brainstorm and where users ask questions or discuss best practices.
Feast users should join #feast-general
or #feast-beginners
to ask questions
Feast developers / contributors should join #feast-development
Mailing list: We have both a user and developer mailing list.
Feast users should join feast-discuss@googlegroups.com group by clicking here.
Feast developers / contributors should join feast-dev@googlegroups.com group by clicking here.
Community Calendar: Includes community calls and design meetings.
Google Folder: 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.
Feast Linux Foundation Wiki: Our LFAI wiki page contains links to resources for contributors and maintainers.
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? Create an issue on GitHub.
StackOverflow: Need to ask a question on how to use Feast? We also monitor and respond to StackOverflow.
We have a user and contributor community call every two weeks (US & EU friendly).
Please join the above Feast user groups in order to see calendar invites to the community calls
Tuesday 10:00 am to 10:30 am PST
Meeting notes (incl recordings): https://bit.ly/feast-notes
We also have a #feast-development
community call every two weeks, where we discuss contributions + brainstorm best practices.
Tuesday 8:00 am to 8:30 am PST
Meeting notes (incl recordings): Feast Development Biweekly
The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.
Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev
, staging
, prod
).
For offline use cases that only rely on batch data, Feast does not need to ingest data and can query your existing data (leveraging a compute engine, whether it be a data warehouse or (experimental) Spark / Trino). Feast can help manage pushing streaming features to a batch source to make features available for training.
For online use cases, Feast supports ingesting features from batch sources to make them available online (through a process called materialization), and pushing streaming features to make them available both offline / online. We explore this more in the next concept page (Data ingestion)
Features are registered as code in a version controlled repository, and tie to data sources + model versions via the concepts of entities, feature views, and feature services. We explore these concepts more in the upcoming concept pages. These features are then stored in a registry, which can be accessed across users and services. The features can then be retrieved via SDK API methods or via a deployed feature server which exposes endpoints to query for online features (to power real time models).
Feast supports several patterns of feature retrieval.
Training data generation
Fetching user and item features for (user, item) pairs when training a production recommendation model
get_historical_features
Offline feature retrieval for batch predictions
Predicting user churn for all users on a daily basis
get_historical_features
Online feature retrieval for real-time model predictions
Fetching pre-computed features to predict whether a real-time credit card transaction is fraudulent
get_online_features