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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 12)
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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 11)
Systematic search strategies, MLflow-tracked runs, and the art of comparing hundreds of experiments to extract your best-performing model. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 10)
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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 9)
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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 8)
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 7)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
