Resources and insights

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.

Copying the same job for dev, staging, and prod invites drift and leaked secrets. Here’s how serverless environment variables let one job definition carry different settings per task, with a working three-task demo.

Classic change data feed saves an extra copy of every change, which adds storage and slows updates. Hubert Dudek benchmarks the automatic alternative on a 5-million-row table and walks through how it works.