20. Deployment and Monitoring of Machine Learning Models

Sections

Navigate through the learning materials and practice exercises.

1. 20

   Deployment And Monitoring Of Machine Learning Models

   Learn Practice

   This section discusses the crucial process of deploying and monitoring...

2. 20.1

   Understanding Model Deployment

   Learn Practice

   Model deployment integrates machine learning models into production...

3. 20.1.1

   What Is Deployment?

   Learn Practice

   Model deployment integrates machine learning models into production...

4. 20.1.2

   Deployment Scenarios

   Learn Practice

   This section outlines various deployment scenarios for machine learning...

5. 20.2

   Infrastructure And Tools For Deployment

   Learn Practice

   This section covers the essential infrastructure and tools needed for...

6. 20.2.1

   Model Serialization Formats

   Learn Practice

   This section provides an overview of various model serialization formats...

7. 20.2.2

   Serving Frameworks

   Learn Practice

   This section explains various frameworks used for serving machine learning...

8. 20.2.3

   Containers And Orchestration

   Learn Practice

   Containers play a vital role in packaging machine learning models, while...

9. 20.2.4

   Serverless Deployments

   Learn Practice

   Serverless deployments provide scalable, cost-efficient options for...

10. 20.3

    Building A Production Pipeline

    Learn Practice

    This section discusses the importance of Continuous Integration and...

11. 20.3.1

    Ci/cd For Ml (Mlops)

    Learn Practice

    CI/CD for ML (MLOps) focuses on automating the process of building, testing,...

12. 20.3.2

    Model Registry

    Learn Practice

    The Model Registry is a centralized repository for managing machine learning...

13. 20.4

    Monitoring Models In Production

    Learn Practice

    Monitoring machine learning models is essential for ensuring their accuracy...

14. 20.4.1

    Why Monitoring Is Crucial

    Learn Practice

    Monitoring machine learning models post-deployment is essential to maintain...

15. 20.4.2

    What To Monitor

    Learn Practice

    This section highlights the critical factors to monitor in machine learning...

16. 20.4.3

    Tools For Monitoring

    Learn Practice

    This section discusses essential tools for monitoring machine learning...

17. 20.5

    Model Retraining And Feedback Loops

    Learn Practice

    Model retraining and feedback loops are essential for maintaining the...

18. 20.5.1

    Model Lifecycle Management

    Learn Practice

    Model Lifecycle Management focuses on the importance of retraining models...

19. 20.5.2

    Incorporating Feedback

    Learn Practice

    Incorporating feedback is essential for enhancing machine learning models by...

20. 20.6

    Best Practices And Challenges

    Learn Practice

    This section outlines the best practices for deploying machine learning...

21. 20.6.1

    Best Practices

    Learn Practice

    This section presents best practices for deploying and monitoring machine...

22. 20.6.2

    Common Challenges

    Learn Practice

    This section discusses the common challenges faced during the deployment and...