Stop running MLflow and your training script in separate terminals. Today you'll wire them together with Docker Compose , one command, a full local ML platform, ready to log every experiment automatically.
Multi-stage builds, .dockerignore hygiene, layer caching, and dependency pinning , the engineering discipline that separates a "it works" image from one you'd actually ship to production.
Containerize your ML code so it runs identically everywhere — on your laptop, on a teammate's machine, and in production — with zero "works on my machine" surprises.
Systematic search strategies, MLflow-tracked runs, and the art of comparing hundreds of experiments to extract your best-performing model.
Stop running ML code in isolation. MLflow Projects make your experiments reproducible on any machine — while the Model Registry gives your models a professional lifecycle: from experiment to staging to production.
Stop losing track of your best model. Learn how MLflow turns chaotic trial-and-error into a reproducible, queryable history of every experiment you've ever run.
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
