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From Model to Production: Building a Governed ML Lifecycle on Databricks
Most ML teams can train a model, but far fewer can operate one. This post shows how to build a governed ML lifecycle on Databricks, with automated validation, champion-challenger promotion, one-swap rollback and continuous monitoring, using MLflow and Unity Catalog.
Databricks Docker: From runtime CI/CD to compliance
Databricks now supports 12 official Docker base images, and you can build your own on top of them. Custom containers let data engineering teams control the full runtime (including native binaries, enterprise certificates, internal packages, and offline environments) not just Python dependencies. This post walks through how to build, test, and deploy a custom Databricks container, with nine production use cases from compliance runtimes to DuckDB integration.