Introduction
Feast (Feature Store) is a customizable operational data system that re-uses existing infrastructure to manage and serve machine learning features to realtime models.
Feast allows ML platform teams to:
Make features consistently available for training and serving by managing an offline store (to process historical data for scale-out batch scoring or model training), a low-latency online store (to power real-time prediction), and a battle-tested feature server (to serve pre-computed features online).
Avoid data leakage by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensure that future feature values do not leak to models during training.
Decouple ML from data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch models to realtime models, and from one data infra system to another.
Who is Feast for?
Feast helps ML platform teams with DevOps experience productionize real-time models. Feast can also help these teams build towards a feature platform that improves collaboration between engineers and data scientists.
Feast is likely not the right tool if you
are in an organization that’s just getting started with ML and is not yet sure what the business impact of ML is
rely primarily on unstructured data
need very low latency feature retrieval (e.g. p99 feature retrieval << 10ms)
What Feast is not?
an / system: Feast is not (and does not plan to become) a general purpose data transformation or pipelining system. Users often leverage tools like to manage upstream data transformations.
a data orchestration tool: Feast does not manage or orchestrate complex workflow DAGs. It relies on upstream data pipelines to produce feature values and integrations with tools like to make features consistently available.
a 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.
Feast does not fully solve
reproducible model training / model backtesting / experiment management: Feast captures feature and model metadata, but does not version-control datasets / labels or manage train / test splits. Other tools like , , and are better suited for this.
batch + streaming feature engineering: Feast primarily processes already transformed feature values (though it offers experimental light-weight transformations). Users usually integrate Feast with upstream systems (e.g. existing ETL/ELT pipelines). is a more fully featured feature platform which addresses these needs.
Example use cases
Many companies have used Feast to power real-world ML use cases such as:
Personalizing online recommendations by leveraging pre-computed historical user or item features.
Online fraud detection, using features that compare against (pre-computed) historical transaction patterns
Churn prediction (an offline model), generating feature values for all users at a fixed cadence in batch
How can I get started?
Explore the following resources to get started with Feast:
is the fastest way to get started with Feast
describes all important Feast API concepts
describes Feast's overall architecture.
