Phase 8: MLOps & Production

Learn how to transform local scripts into enterprise-grade cloud applications. You will master containerization, automated CI/CD pipelines, and robust cloud monitoring tools to deploy and scale AI models reliably for thousands of active users.

The curriculum is public. Reading these lessons requires access to the corresponding paid track. Compare access and pricing.

What will you learn?

• Deploy ML models
• Monitor production systems

Lessons and practice

1. Lesson 1: Deploying ML Models — From Notebook to Production
2. Model Deployment & Production Systems - Practice Questions
3. Model Deployment & Production Systems - Interview Questions & Answers
4. Model Deployment & Production - Complete Cheatsheet
5. Lesson 1: Data Engineering — Pipelines That Feed ML
6. Data Engineering - Practice
7. Data Engineering - Interview Preparation
8. Data Engineering - Cheatsheet
9. Lesson 1: MLOps — Keeping Models Alive in Production
10. MLOps & Production ML - Practice
11. MLOps & Production ML - Interview Preparation
12. MLOps & Production ML - Cheatsheet
13. Lesson 1: Cloud ML Infrastructure — Training and Serving at Scale
14. Cloud ML Infrastructure Practice Exercises
15. Cloud ML Infrastructure Interview Preparation
16. Cloud ML Infrastructure Cheatsheet
17. Lesson 1: Security & Compliance in ML Systems
18. ML Security & Compliance — Practice Exercises
19. ML Security & Compliance — Cheatsheet
20. ML Security & Compliance — Interview Preparation
21. Lesson 1: System Design for ML — Architecture That Scales
22. Machine Learning & MLOps System Design Practice Exercises
23. System Design - Quick Reference Cheatsheet
24. System Design - Interview Preparation Guide
25. Lesson 1: DevOps & Containerisation for ML Engineers
26. DevOps & Containerization - Practice Exercises
27. DevOps & Containerization - Quick Reference Cheatsheet
28. DevOps & Containerization - Interview Preparation Guide

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