Azure Data Scientist Associate: Machine Learning Orchestration & Deployment

Azure    |    Intermediate
  • 11 videos | 1h 12m 57s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 76 users Rating 4.3 of 76 users (76)
Azure Machine Learning Studio provides DevOps support in the form of orchestrating machine learning pipelines. In this course, you'll learn to create, publish, and schedule machine learning pipelines. First, you'll examine Azure Machine Learning pipelines and how they are used to build, optimize, and manage machine learning workflows. Next, you'll explore how to use the Azure Machine Learning SDK to create and run machine learning pipelines. You'll learn how to use a pipeline to import, transform, and move data between steps, as well as how to publish and track pipelines and use triggers to schedule a machine learning pipeline based on some event. Finally, you'll learn techniques for troubleshooting and debugging machine learning pipelines This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe azure machine learning pipelines and how they are used to build, optimize, and manage machine learning workflows
    Use azure machine learning pipelines to import, transform, and move data between steps
    Use the azure machine learning sdk to create and run machine learning pipelines
    Publish and track machine learning pipelines and share them with others
    Schedule a machine learning pipeline based on an elapsed time or file system changes
  • Describe techniques for troubleshooting and debugging machine learning pipelines
    Deploy a model as a real-time service to different compute targets
    Consume a real-time service that can be used to predict labels
    Troubleshoot a failed deployment using various techniques
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 25s
  • 8m 21s
  • Locked
    3.  Moving Data Using Machine Learning Pipelines
    10m 31s
  • Locked
    4.  Creating Machine Learning Pipelines
    7m 48s
  • Locked
    5.  Publishing Machine Learning Pipelines
    7m 1s
  • Locked
    6.  Triggering Machine Learning Pipelines
    6m 38s
  • Locked
    7.  Troubleshooting Machine Learning Pipelines
    7m 11s
  • Locked
    8.  Deploying Real-time Services
    8m 46s
  • Locked
    9.  Consuming Real-time Services
    6m 33s
  • Locked
    10.  Troubleshooting Service Deployments
    8m 3s
  • Locked
    11.  Course Summary
    40s

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