MLOps for Vibe Coders — The Critical Bottleneck Docker, Kubernetes, and CI/CD Alone Don't Solve

DESCRIPTION:As companies move AI from pilots to production, MLOps has genuinely become the critical bottleneck — and this course assumes your Docker, Kubernetes, CI/CD, and Monitoring courses as direct foundations, adding the ML-specific layer those courses were never designed to cover.From experiment tracking with MLflow and model versioning (genuinely distinct from Git's code versioning) through data and model drift — the problem unique to ML where a model can silently "rot" even without any code change — pipeline orchestration with Airflow, containerized model serving, continuous training, production monitoring with ML-specific metrics, and a dedicated compliance and governance module, this course ends with a complete, self-sustaining MLOps pipeline combining every concept.Interactive format with quizzes on real conceptual traps: why a model's code staying identical doesn't mean its behavior stays consistent, why Demographic Parity and Equalized Odds can mathematically conflict, and why "the service is up" genuinely isn't the same as "the model is working well."WHAT'S INSIDE (10 Modules):✦ Module 1 — Why MLOps: the critical bottleneck Docker/K8s/CI/CD alone don't solve ✦ Module 2 — Experiment tracking with MLflow ✦ Module 3 — Model versioning & the model registry ✦ Module 4 — Data & model drift ✦ Module 5 — ML pipeline orchestration with Airflow ✦ Module 6 — Containerizing & serving models ✦ Module 7 — CI/CD for ML — Continuous Training ✦ Module 8 — Monitoring ML models in production ✦ Module 9 — Compliance & governance in ML pipelines ✦ Module 10 — A complete MLOps pipeline: from experiment to productionWHO THIS IS FOR:→ Vibe coders who've completed Docker, Kubernetes, and CI/CD and want the ML-specific layer → Anyone who's deployed a model without tracking whether its accuracy holds up over time → Builders wanting to understand MLflow, model registries, and drift detection concretely → Developers preparing for MLOps or ML platform engineering rolesFAQ:Q: Do I need the Docker, Kubernetes, or CI/CD courses first? A: Yes, genuinely recommended — this course builds directly on those foundations rather than re-teaching them.Q: Is this only for large companies with dedicated ML teams? A: No — the core patterns (tracking, versioning, drift detection) genuinely apply at any scale, from a solo builder's first deployed model upward.Q: What format is this? A: An interactive React web app you open in your browser. Works on desktop and mobile.

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