SophiArch
AdvancedML401

MLOps & Model Deployment

Close the gap between a working model and a reliable production system. Learn how to package, deploy, monitor, and govern ML models — and develop the judgment to catch the silent failures that AI tools introduce at every stage of the deployment pipeline.

Lessons are AI-assisted and human-reviewed. Learn more.

Syllabus

Production ML Fundamentals

01
The Production Gap: From Notebook to ProductionFree preview
35 min
02
Model Packaging: What Breaks When You Save
40 min
03
Training-Serving Skew: Silent Failures in Feature Pipelines
45 min

Deployment Infrastructure

04
Containerising ML Models: Docker for Production
60 min
05
Kubernetes for ML: Orchestration and Health Checks
65 min
06
CI/CD for ML: Automating Validation, Not Just Tests
45 min

Monitoring and Governance

07
Model Monitoring: Detecting Drift Before Your Users Do
50 min
08
Retraining Strategies and Model Governance
45 min