ml-pipeline-setup

To build an end-to-end machine learning pipeline on Databricks, you orchestrate feature engineering, model training, and batch inference while managing MLflow experiment tracking and Unity Catalog model registration. This ensures reproducibility and scalability across the entire workflow.

MLflow integrates with Unity Catalog for model registry by tracking experiments and registering trained models directly within the catalog. This combination enforces production best practices for governance, reproducibility, and scalability throughout the machine learning pipeline.

To ensure training-serving consistency for batch inference, leverage the Databricks Feature Store during feature engineering. This integration guarantees that the features used during model training match the features generated during batch inference on new data.

Yes, you need Databricks Asset Bundles to automate ML workflows because they serve as a core dependency for structuring and deploying the pipeline components. Bundles work alongside naming-tagging standards and autonomous operations to maintain production-grade consistency.

Yes, you can use this approach to generate predictions from Gold layer data. The workflow automates the entire process of building and deploying a predictive model, moving from raw data in the Gold layer to generating predictions on new data while following best practices.

You need the Databricks Feature Store for feature engineering when creating feature tables that require strict training-serving consistency. It integrates directly into the machine learning pipeline to ensure that feature definitions remain consistent from model training through batch inference.