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Deploying and Managing Models in Microsoft Azure

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Axel Sirota

1:44:42

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  • 01. Course Overview.mp4
    02:01
  • 02. The Lifecycle of a Model from Creation to Deployment.mp4
    02:43
  • 03. Introducing Globomantics Case Study.mp4
    00:58
  • 04. Getting the Best out of This Course.mp4
    02:37
  • 05. Outline of the Course.mp4
    01:12
  • 06. Deployment Options in Azure Machine Learning.mp4
    03:48
  • 07. Real-time and Batch Endpoints.mp4
    03:42
  • 08. Demo-Deploying Locally - Part 1.mp4
    02:35
  • 09. Demo-Deploying Locally - Part 2.mp4
    08:15
  • 10. Demo-Deploying Locally - Part 3.mp4
    01:08
  • 11. Demo-Deploy an Online Endpoint.mp4
    05:07
  • 12. Demo-Perform A B Testing in the Studio - Part 1.mp4
    03:27
  • 13. Demo-Perform A B Testing in the Studio - Part 2.mp4
    05:07
  • 14. Demo-Deploying a Real-time A B Test to Kubernetes - Part 1.mp4
    03:49
  • 15. Demo-Deploying a Real-time A B Test to Kubernetes - Part 2.mp4
    05:26
  • 16. Key Takeaways and Tips.mp4
    01:49
  • 17. CI CD in Machine Learning.mp4
    05:50
  • 18. Different Types of Pipelines in Azure.mp4
    05:17
  • 19. Demo-Creating an Azure Pipelines Project - Part1.mp4
    08:04
  • 20. Demo-Creating an Azure Pipelines Project - Part2.mp4
    04:54
  • 21. Demo-Creating an Azure ML Pipeline - Part 1.mp4
    05:32
  • 22. Demo-Creating an Azure ML Pipeline - Part 2.mp4
    05:23
  • 23. Demo-Creating an Azure ML Pipeline - Part 3.mp4
    03:17
  • 24. Key Takeaways and Tips.mp4
    02:03
  • 25. What Is MLOps.mp4
    04:16
  • 26. Introducing Model Decay and Data Drift.mp4
    03:42
  • 27. Key Takeaways and Tips.mp4
    02:40
  • Description


    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.

    What You'll Learn?


      Once a model is created, we need to deploy it. Moreover, we need to ensure their performance does not decay and we keep track of the current model in production. In this course, Deploying and Managing Models in Microsoft Azure, you’ll learn the best practices to deploy, manage, and retrain machine learning models in Azure. First, you’ll explore how to deploy machine learning models in Azure. Next, you’ll discover how to create a retraining pipeline with Azure ML Pipelines. Finally, you’ll learn about MLOps practices to take into account. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations in Azure needed to deploy, manage, and retrain machine learning models in Azure.

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    Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.
    Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.
    • language english
    • Training sessions 27
    • duration 1:44:42
    • level average
    • Release Date 2022/12/12