Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps

Deploy, manage, and scale Machine Learning models with MLOps effortlessly Key Features ● Explore several ways to build

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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

Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps

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