Get Models Into Production, and Keep Them There
Two courses — MLOps Foundations, then Production MLOps. Each stands on its own, and each ends in a project you can show, not just a certificate.
How the Track Works
Two courses, back to back, two months each: MLOps Foundations, then Production MLOps. You’re never locked in beyond two months — each course ends in its own final project or capstone and its own certificate.
The Two Courses
MLOps Foundations — 2 months, 24 sessions —
CI/CD for ML, Docker, model versioning (MLflow/DVC), basic monitoring. Ends in a final project that operationalizes a real ML model end-to-end.
Production MLOps — 2 months, 24 sessions —
Kubernetes basics, model serving, scaling, observability, incident response. Ends in a capstone that mirrors a real on-call rotation — deploy, scale, monitor, and respond to a simulated incident.
Where to Start
- New to MLOps or DevOps for ML? Start with MLOps Foundations.
- Already comfortable with Git, Docker, and the core MLOps tool chain (versioning, tracking, CI/CD)? Production MLOps might be the better place to jump in.