AutoML · Databricks
Databricks AutoML: Lakehouse automation
Auto-generated notebooks, feature engineering, MLflow tracking, and deployment
Databricks AutoML creates baseline models and feature-engineering notebooks on the Lakehouse for classification, regression, and time series. It integrates Delta/Unity Catalog, Feature Store, MLflow tracking, and serving so teams can iterate quickly and ship to production.
Editable notebooks
Generates notebooks for baseline models and feature prep that data scientists can extend.
Lakehouse integration
Works with Delta tables and Unity Catalog governance, with Feature Store for reusable features.
MLflow MLOps
Built-in experiment tracking, model registry, serving endpoints, and batch inference.
Task coverage
AutoML for classification, regression, and time series with automatic hyperparameter search.
适用场景 / Use cases
- Quickly producing explainable baseline notebooks
- AutoML + MLOps on the Lakehouse
- Forecasting, pricing, and scoring models
- Empowering analysts/citizen data scientists on Databricks