From the course: MLOps with Databricks
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Introduction to feature engineering - Databricks Tutorial
From the course: MLOps with Databricks
Introduction to feature engineering
- [Instructor] A feature store is a centralized repository for storing and sharing machine learning model features. It's particularly useful when the same features are used across multiple models, and to ensure that the same transformation is applied for both model training and model scoring. Workspace feature store is marked as legacy, and Feature Engineering Unity Catalog is a recommended approach for handling model features. Databricks provides Python API to interact with the Databricks Feature Engineering Unity Catalog. Databricks Feature Engineering covers multiple concepts, which I use in this course. A FeatureTable; a table stored as a Delta table in Unity Catalog that contains machine learning model features. FeatureFunction; a class to specify a Python user-defined function in the Unity Catalog. FeatureLookup; a class to specify how to lookup features in the feature table. TrainingSet; a class that defines how to construct a training set using feature table, FeatureFunction…
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