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Creating a Time Series ML Model with AtScale’s AI-Link, Snowflake, and Feast
See how AtScale has built an integration with Feast, enabling users to operationalize data in AtScale’s semantic layer for ML use cases.
Serve Your ML Features in Production
Feast is the leading open source feature store that automates the last mile in your production ML data pipelines. It allows data teams to serve features consistently for offline training and online inference.
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Why Feast?
Serve your features online
Feast automates the process of storing and serving your features from an online store to support real-time predictions.
Ensure consistency across training and serving
Feast guarantees you’re serving the same data to models during training and inference, eliminating training-serving skew.
Integrate with your existing tooling and infrastructure
Feast integrates with your existing data pipelines and ML tooling. It runs on top of cloud managed services, reusing your existing infrastructure and spinning up resources as needed.
Standardize your data workflows across teams
Feast brings standardization and consistency to your data engineering workflows across models and teams. Many teams use Feast as the foundation of their internal ML platforms.
Teams running or contributing to Feast
FAQ
What is a feature store?
We wrote an article on this! What is a Feature Store?
Is Feast a feature computation system?
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.
For more details, please see the quickstart guide
What data sources / clouds does Feast support?
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).
See more details at third party integrations
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)
See our benchmark post (which comes with a benchmark suite on GitHub). In benchmarks, we’ve seen single entity p99 read times to be <10 ms with a Python feature server on Redis and <4 ms with a Go feature server. Feast also is performant (p99 < 20ms in benchmarks) in batch online retrieval.
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.
