This topic groups Databricks articles around production delivery: MLOps workflows, GitHub Actions, MLflow, feature engineering and lakehouse-side platform decisions.
Use it as a reading path for teams that already have data gravity in Databricks and need stronger deployment, validation and operating boundaries.
Feature store alternatives for production ML: when feature tables, shared transformation libraries or warehouse-first patterns are enough, and when a feature store earns its cost.
ML release management needs more than a registered model. Learn how MLflow supports versioning, validation and rollback on Azure Databricks.
Databricks MLOps Stacks are a practical starting point for production ML. Learn how to grow the template into a team standard for delivery, release and operations.
Medallion architecture for engineers: design raw data, trusted views and published data products with clear contracts, replay paths and ownership.
A production CI/CD pattern for Databricks MLOps using GitHub Actions, Declarative Automation Bundles, validation gates and environment promotion.
Compare MLflow delivery patterns for Azure ML and Databricks: model lifecycle, serving, validation, environment promotion and production operations.
A practical Databricks MCP server tutorial: build a custom MCP server in Python, keep the tool layer small, and deploy it on Databricks Apps with a Delta-backed GTD assistant.