Deploying and Managing Models in Microsoft Azure

سرفصل های دوره

To deliver a great idea, one needs to deploy your ML models as well as ensure they keep up to date. This course will teach you the best practices to deploy, manage, and retrain machine learning models in Azure.

01. Course Overview

01. Course Overview

02. Performing Machine Learning Operations with Azure

02. The Lifecycle of a Model from Creation to Deployment

03. Introducing Globomantics Case Study

04. Getting the Best out of This Course

05. Outline of the Course

03. Deploying a model in Azure ML

06. Deployment Options in Azure Machine Learning

07. Real-time and Batch Endpoints

08. Demo-Deploying Locally - Part 1

09. Demo-Deploying Locally - Part 2

10. Demo-Deploying Locally - Part 3

11. Demo-Deploy an Online Endpoint

12. Demo-Perform A B Testing in the Studio - Part 1

13. Demo-Perform A B Testing in the Studio - Part 2

14. Demo-Deploying a Real-time A B Test to Kubernetes - Part 1

15. Demo-Deploying a Real-time A B Test to Kubernetes - Part 2

16. Key Takeaways and Tips

04. Building and Operationalizing Pipelines with Azure

17. CI CD in Machine Learning

18. Different Types of Pipelines in Azure

19. Demo-Creating an Azure Pipelines Project - Part1

20. Demo-Creating an Azure Pipelines Project - Part2

21. Demo-Creating an Azure ML Pipeline - Part 1

22. Demo-Creating an Azure ML Pipeline - Part 2

23. Demo-Creating an Azure ML Pipeline - Part 3

24. Key Takeaways and Tips

05. Machine Learning Operations in Azure

25. What Is MLOps

26. Introducing Model Decay and Data Drift

27. Key Takeaways and Tips

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تولید کننده: PluralSight