MLOps & LLMOps

AI/ML Engineering Track | Phase 5

Best for: learners who already know how to build AI workflows and now need them to be reproducible, reviewable, and operable.

This phase is the bridge from experiments to systems: containers, pipelines, experiment tracking, serving, monitoring, and the notebook-to-production handoff.

Modules

#	Module
1.1	ML DevOps Foundations
1.2	Docker for ML
1.3	CI/CD for ML
1.4	Kubernetes for ML
1.5	Advanced Kubernetes
1.6	Experiment Tracking
1.7	Data Pipelines
1.8	ML Pipelines
1.9	Model Serving
1.10	ML Monitoring
1.11	Notebooks to Production for ML/LLMs
1.12	Small-Team Private AI Platform

Good Entry Points

• Experiment Tracking if you are still losing run history and artifact lineage
• Notebooks to Production for ML/LLMs if your work still lives mostly in notebooks
• Model Serving if your next step is deployment rather than training depth

After This Phase

• go to AI Infrastructure for serving, local inference, and operational tradeoffs
• go to Platform Engineering: Data & AI for the larger operational picture