Scaling Feature Engineering Pipelines with Feast and Ray
Summary
The article explores the integration of feature stores like Feast and distributed computing frameworks such as Ray to enhance production machine learning systems. It highlights the benefits of scaling feature engineering pipelines for improved efficiency and performance.
Key Insights
What is a feature store like Feast, and why is it used in machine learning?
A feature store like Feast is a centralized repository that manages machine learning features, including an internal registry for metadata, an offline store for historical training data, and an online store for low-latency real-time inference data, enabling teams to discover, share, and reuse features efficiently across projects.[3]
Sources:
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How does Ray integrate with Feast to scale feature engineering?
Ray integrates with Feast as a distributed compute engine for executing feature pipelines, including transformations, aggregations, joins, and materializations, and as an offline store for data I/O; it supports automatic join strategies (broadcast for small data <100MB, distributed windowed for large data), lazy evaluation, and deployment modes like local, remote clusters, or Kubernetes via KubeRay.[1][2][5]