Azure AI Fundamentals: Evaluating Models with the ML Designer

Azure 2020    |    Beginner
  • 16 Videos | 2h 2m 34s
  • Includes Assessment
  • Earns a Badge
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In order to build a powerful and useful machine learning deployment, you must be able to evaluate and verify the AI model and data, as well as the accuracy and effectiveness of its predictions. Azure Machine Learning Studio and the Designer provide multiple easy-to-use methods for evaluating and scoring a model. In this course, you'll learn how to score and evaluate models and interpret and evaluate the results from some common models. You'll also explore how to create an inference pipeline, add web service output to provide external access to the model, and deploy and test a predictive web service. 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
    add a Scoring model component in the ML Designer
    describe model evaluation types like MAE and R2
    use an evaluator on a model and interpret the metrics
    run and monitor a complete pipeline
    analyze the evaluation results in the output and logs section in the ML Designer
    identify and investigate the details of the evaluation results
    visualize the scoring data from the Scoring model
  • investigate the logs and results that are significant when running a Regression model
    interpret the results from running a Classification model
    interpret the results and logs form running a Clustering model
    create an inference pipeline using a Python script
    add a web service output to provide external access to the model
    deploy the model as a predictive service
    test the predictive service from an external app
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 27s
    UP NEXT
  • Playable
    2. 
    Adding and Using Scoring on Models
    7m 32s
  • Locked
    3. 
    Model Evaluation Types
    7m 14s
    FREE ACCESS
  • Locked
    4. 
    Using Evaluators on the Model
    6m 10s
    FREE ACCESS
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    5. 
    Running a Pipeline
    10m 44s
    FREE ACCESS
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    6. 
    Analyzing the Evaluation Results Output and Logs
    8m 32s
    FREE ACCESS
  • Locked
    7. 
    Exploring the Evaluation Results Details
    9m 24s
    FREE ACCESS
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    8. 
    Visualizing the Data in the Scoring Model
    9m 2s
    FREE ACCESS
  • Locked
    9. 
    Investigating Results from a Regression Model
    10m 17s
    FREE ACCESS
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    10. 
    Interpreting Results from a Classification Model
    10m 22s
    FREE ACCESS
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    11. 
    Investigating Results from a Clustering Model
    10m 50s
    FREE ACCESS
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    12. 
    Creating an Inference Pipeline
    10m 56s
    FREE ACCESS
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    13. 
    Adding a Web Service Output
    6m 59s
    FREE ACCESS
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    14. 
    Deploying a Predictive Service
    6m 29s
    FREE ACCESS
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    15. 
    Testing a Predictive Service
    5m 41s
    FREE ACCESS
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    16. 
    Course Summary
    55s
    FREE ACCESS

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