Engineering MLOps

Table of Contents

1. Fundamentals of MLOps Workflow
2. Characterizing your Machine learning problem
3. Code Meets Data
4. Machine Learning Pipelines
5. Model evaluation and packaging
6. Key principles for deploying your ML system
7. Building robust CI and CD pipelines
8. APIs and microservice Management
9. Testing and Securing Your ML Solution
10. Essentials of Production Release
11. Key principles for monitoring your ML system
12. Model Serving and Monitoring
13. Governing the ML system for Continual Learning