Table of Contents

  1. Python 101
  2. Git and GitHub Fundamentals
  3. Challenges in ML Model Deployment
  4. Packaging ML Models
  5. MLflow-Platform to Manage the ML Life Cycle
  6. Docker for ML
  7. Build ML Web Apps Using API
  8. Build Native ML Apps
  9. CI/CD for ML
  10. Deploying ML Models on Heroku
  11. Deploying ML Models on Microsoft Azure
  12. Deploying ML Models on Google Cloud Platform
  13. Deploying ML Models on Amazon Web Services
  14. Monitoring and Debugging
  15. Post-Productionizing ML Models