Azure ML Feature Store with Terraform: Managed Feature Materialization for Training and Inference
This article explains how to use Azure ML Feature Store, a specialized workspace that manages feature engineering, offline materialization, and online serving. Terraform is used to provision the necessary infrastructure.
Why it matters
The Azure ML Feature Store helps to ensure that machine learning models are trained and deployed with consistent, high-quality features, improving model accuracy and reliability.
Key Points
- 1Azure ML Feature Store is a specialized workspace for managing feature engineering and materialization
- 2It provides offline storage (ADLS/Blob) for historical features and online storage (Redis) for low-latency inference
- 3Feature sets include transformation code, ensuring consistent features for both training and inference
- 4Terraform is used to provision the Feature Store infrastructure, including the workspace, offline store, and online store
Details
The Azure ML Feature Store is a specialized workspace within the Azure ML platform that is designed to manage feature engineering, offline materialization to storage, and online serving with Redis. It provides a consistent way to define feature sets, including the transformation code, and ensures that the same features are used for both training and inference. This helps to eliminate training-serving skew, where the features used for model training differ from those used for real-time inference. The article explains the key components of the Feature Store architecture, including entities, feature sets, the offline store, the online store, and the materialization process. It then demonstrates how to use Terraform to provision the necessary infrastructure, including the Feature Store workspace, the offline storage account, and the online Redis cache.
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