Enterprise Services: Machine Learning Implementation on Microsoft Azure

Machine Learning
  • 14 Videos | 1h 18m 36s
  • Includes Assessment
  • Earns a Badge
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Explore the features and operational benefits of using a cloud platform to implement ML (machine learning) by using Microsoft Azure and Amazon SageMaker, in this 14-video course. First, you will learn how to use Microsoft Azure ML tools, services, and capabilities, and  how to examine MLOps (machine learning and operations) to manage, deploy, and monitor models for quality and consistency. You will create Azure Machine Learning workspaces, and learn to configure development environments, build, and manage ML pipelines, to work with data sets, train models, and projects. You will develop and deploy predictive analytic solutions using the Azure Machine Learning Service visual interface, and work with Azure Machine Learning R Notebooks to fit and publish models. You will learn to enable CI/CD (continuous integration and continuous delivery) with Azure Pipelines, and examine ML tools in AWS (Amazon Web Services) SageMaker, and how to use Amazon's ML console. Finally, you will learn to track code from Azure Repos or GitHub, trigger release pipelines, and automate ML deployments by using Azure Pipelines.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    describe Azure machine learning tools, services, and capabilities
    compare the capabilities of Azure Machine Learning Studio and Azure Machine Learning Service
    create Azure Machine Learning Service workspaces and configure development environments for Azure Machine Learning
    build and manage machine learning pipelines with Azure Machine Learning Service
    launch the Microsoft Azure Machine Learning Studio and work with datasets, train models, and projects
    use the Azure Machine Learning Service visual interface to develop and deploy predictive analytic solutions
  • access, transform, and join data using Azure Open Datasets and train automated machine learning regression models to calculate model accuracy
    describe the capabilities of MLOps with focus on managing, deploying, and monitoring models using Azure Machine Learning Service to improve the quality and consistency of machine learning solutions
    work with Azure Machine Learning R Notebooks to fit models and publish models as web services
    build predictive pipelines, incorporating Azure Data Lake and Azure Machine Learning
    enable CI/CD for machine learning projects with Azure Pipelines
    use the ML extension of Visual Studio from Microsoft DevLabs to track code from Azure Repos or GitHub, trigger release pipelines, and automate machine learning deployments using Azure Pipelines
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m
    UP NEXT
  • Playable
    2. 
    Azure Machine Learning Tools and Capabilities
    8m 48s
  • Locked
    3. 
    Comparing Azure ML Studio and Azure ML Service
    8m 35s
  • Locked
    4. 
    Creating & Configuring Azure ML Service Workspace
    3m 36s
  • Locked
    5. 
    Building ML Pipelines with Azure ML Service
    4m 13s
  • Locked
    6. 
    Working with Azure ML Studio
    4m 40s
  • Locked
    7. 
    Using Azure ML Service Visual Interface
    5m 38s
  • Locked
    8. 
    Working with Azure Open Datasets
    6m 21s
  • Locked
    9. 
    Azure MLOps
    5m 17s
  • Locked
    10. 
    Azure ML R Notebooks
    8m 14s
  • Locked
    11. 
    Pipelines with Azure Data Lake and Azure ML
    6m 2s
  • Locked
    12. 
    CI/CD for Machine Learning with Azure Pipeline
    5m 11s
  • Locked
    13. 
    Using Microsoft DevLabs Extension
    3m 47s
  • Locked
    14. 
    Course Summary
    1m 14s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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