Azure AI Fundamentals: Using Azure Machine Learning Studio

Azure 2020    |    Beginner
  • 18 Videos | 1h 37m 46s
  • Includes Assessment
  • Earns a Badge
The Azure Machine Learning Studio is a complete web tool and graphical user interface for building, managing, deploying, evaluating, and testing machine learning algorithms and workloads from initial design to final deployment. In this course, you'll investigate the different features of the Azure ML Studio interface and use it to create datasets, ingest data, create models automatically, build prediction services, and finally, manage endpoints for a machine learning model. Furthermore, you'll explore the datastores, compute resources, experiments, pipelines, and model management interfaces that are utilized when working with Azure ML Studio. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    create and configure an Azure Machine Learning workspace
    create and use a compute resource using Azure ML Studio
    create and use a dataset in Azure ML Studio
    ingest data from an Azure Storage source
    ingest data from an Azure Blob storage resource
    label data within a dataset in the Azure ML Studio interface
    identify how to run test scripts manually using Notebook
    use the automated ML model to create an experiment that will automatically find the best-fit model
  • run an automated ML model experiment to find the best-fit model
    evaluate the results of an automated ML model experiment to investigate the best model results
    deploy an automated ML model as a predictive service
    test an automated ML predictive service by using it to get predictions based on test data
    manage and manipulate compute resources and datastores from the Azure ML Studio
    manipulate and configure datasets and experiments, including for other team members, in Azure ML Studio
    manage stored pipelines and models in Azure ML Studio
    manage and configure endpoints in Azure ML Studio
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    56s
    UP NEXT
  • Playable
    2. 
    Creating an Azure ML Workspace
    5m 4s
  • Locked
    3. 
    Creating a Compute Resource Using Azure ML Studio
    5m 11s
  • Locked
    4. 
    Creating and Using a Dataset in Azure ML Studio
    4m 33s
  • Locked
    5. 
    Ingesting Data from Azure Storage
    7m 46s
  • Locked
    6. 
    Ingesting Data from Azure Blob Storage
    6m 57s
  • Locked
    7. 
    Labeling Data in Azure ML Studio
    8m 10s
  • Locked
    8. 
    Running and Testing Scripts Using Notebook
    5m 34s
  • Locked
    9. 
    Creating an Automated ML Model
    5m 25s
  • Locked
    10. 
    Running an Automated Experiment
    4m 32s
  • Locked
    11. 
    Determining the Best Model after an Experiment
    4m 5s
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    12. 
    Deploying a Model as a Predictive Service
    3m 9s
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    13. 
    Testing the Predictive Service
    5m 51s
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    14. 
    Managing Compute Resources and Datastores
    4m 49s
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    15. 
    Managing Datasets and Experiments in Azure ML Studio
    5m 23s
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    16. 
    Managing Pipelines and Models in Azure ML Studio
    6m 36s
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    17. 
    Managing Endpoints in Azure ML Studio
    4m 49s
  • Locked
    18. 
    Course Summary
    56s

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