MLOps Roadmap for Beginners (2026): What to Learn After Basic ML
Updated on February 14, 2026 13 minutes read
Frequently Asked Questions
Do I need DevOps experience before learning MLOps?
No, you can start with practical basics like Git, testing, and Docker. As you deploy projects, you’ll naturally learn the DevOps concepts that matter most.
What should I learn first after basic machine learning?
Start with engineering foundations and reproducibility. Git workflows, packaging, testing, experiment tracking, and data versioning give the biggest early gains.
Is Kubernetes required for entry-level MLOps roles?
Not always, and many teams don’t start there. Learn containers, deployment basics, and CI/CD first, then add Kubernetes when you have a real reason.
What’s the difference between ML engineering and MLOps?
ML engineering often focuses on building models and features that perform well. MLOps focuses on deploying, automating, monitoring, and maintaining ML systems reliably over time.
How can I build an MLOps portfolio as a beginner?
Build one complete project: reproducible training, a deployment (batch or API), CI checks, and monitoring. Document it clearly with a diagram and a short demo so recruiters can review it quickly.