MLOps Roadmap

A comprehensive learning path covering the intersection of machine learning, DevOps, and data engineering. You'll start with Python and ML fundamentals, then master containerization, CI/CD, model deployment, monitoring, and scaling ML systems in production.

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MLOps Roadmap

From ML basics to production-grade ML systems

Foundations0/4
Checkpoint — Python & ML Basics
Data & Experiment Management0/3
Checkpoint — Data Versioning & Experiment Tracking
Containerization & Orchestration0/3
Checkpoint — Docker & Kubernetes for ML
CI/CD & Automation0/3
Checkpoint — Automated ML Pipelines
Model Deployment & Serving0/3
Checkpoint — Deploying Models to Production
Monitoring, Governance & Advanced MLOps0/4
Checkpoint — Production ML Excellence

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Frequently Asked Questions

Yes, this roadmap is free to browse. Individual linked courses may be free or paid.

Each phase builds on the one before it, so following the order is recommended — but you can jump to any topic you want.

This roadmap has 20 topics across 6 phases. Completion time depends on your pace, since every linked course is self-paced.

Each linked course issues its own verified certificate on completion. There is no separate roadmap-level certificate.