Partially. Feast enables on-demand transformations to generate features that combine request data with precomputed features (e.g. time_since_last_purchase), with plans to allow light-weight feature engineering.
Many users use Feast today in combination with a separate system that computes feature values. Most often, these are pipelines written in SQL (e.g. managed with dbt projects) or a Python Dataframe library and scheduled to run periodically.
If you need a managed feature store that provides feature computation, check out Tecton.
How do I install and run Feast?
Feast is a Python library + optional CLI. You can install it using pip.
You might want to periodically run certain Feast commands (e.g. `feast materialize-incremental`, which updates the online store.) We recommend using schedulers such as Airflow or Cloud Composer for this.
Feast supports data sources in all major clouds (AWS, GCP, Azure, Snowflake) and plugins to work with other data sources like Hive.
Feast also manages storing feature data in a more performant online store (e.g. with Redis, DynamoDB, Datastore, Postgres), and enables pushing directly to this (e.g. from streaming sources like Kafka).
What are best practices for using Feast to power production ML systems?
For guidance on how to structure your feature repos, how to setup regular materialization of feature data, and how to deploy Feast in production, see our guide Running Feast in Production
How performant / scalable is Feast?
Feast is designed to work at scale and support low latency online serving. We support different deployment patterns to meet different operational requirements (see guide)
Official benchmarks (RFC) will be released soon and numbers vary by number of features, concurrency of serving, batch size, etc. In benchmarks, we’ve seen single entity p99 read times to be <10 ms with a python feature server on Redis and < 1 ms with a java feature server.
Who uses Feast?
Gojek, Shopify, Salesforce, Twitter, Postmates, Robinhood, Porch, and Zulily are some examples of teams that are currently using the Feast Feature Store.
Many teams use Feast to support ML use cases like fraud detection or recommender systems. Users range from researchers to smaller teams starting their ML platforms to large mature teams like Twitter / Shopify.
Is Feast a database?
No. Feast is a tool that manages data stored in other systems (e.g. BigQuery, Cloud Firestore, Redshift, DynamoDB). It is not a database, but it helps manage data stored in other systems.