mlops-stacks

Why we included this project

Databricks MLOps Stacks is a template that skips the weeks of setup most teams spend before their first real training run. One command generates a project skeleton with unit-tested training and batch inference modules, and ML resources are defined as code through Databricks asset bundles, so infrastructure changes flow through pull requests rather than manual UI clicks. The included GitHub Actions or Azure DevOps workflows run tests in an isolated staging workspace and only promote tested code to production, the kind of discipline small teams usually adopt after something breaks. Data scientists can keep iterating in notebooks without a later refactor, and ML engineers add CI/CD when a project is ready to move. If you are already on Databricks and want an opinionated starting point instead of assembling your own pipeline, this is a solid base to clone and adapt.