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Microsoft

Build and operate machine learning solutions with Azure Machine Learning

Microsoft via Microsoft Learn

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Overview

  • Module 1: Introduction to the Azure Machine Learning SDK

    • In this module, you will learn how to:

    • Provision an Azure Machine Learning workspace.
    • Use tools and interfaces to work with Azure Machine Learning.
    • Run code-based experiments in an Azure Machine Learning workspace.
  • Module 2: Train a machine learning model with Azure Machine Learning

    • In this module, you will learn how to:

    • Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
    • Create reusable, parameterized training scripts.
    • Register trained models.
  • Module 3: Work with Data in Azure Machine Learning
    • Create and use datastores in an Azure Machine Learning workspace.
    • Create and use datasets in an Azure Machine Learning workspace.
  • Module 4: Work with Compute in Azure Machine Learning
    • Work with environments
    • Work with compute targets
  • Module 5: Orchestrate machine learning with pipelines
    • Create Pipeline steps
    • Pass data between steps
    • Publish and run a pipeline
    • Schedule a pipeline
  • Module 6: Deploy real-time machine learning services with Azure Machine Learning

    • In this module, you will learn how to:

    • Deploy a model as a real-time inferencing service.
    • Consume a real-time inferencing service.
    • Troubleshoot service deployment
  • Module 7: Deploy batch inference pipelines with Azure Machine Learning

    • Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.

  • Module 8: Tune hyperparameters with Azure Machine Learning

    • Learn how to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.

  • Module 9: Automate machine learning model selection with Azure Machine Learning

    • In this module, you will learn how to:

    • Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
    • Use automated machine learning to preprocess data for training.
    • Run an automated machine learning experiment.
  • Module 10: Explore differential privacy

    • After completing this module, you'll be able to:

    • Articulate the problem of data privacy
    • Describe how differential privacy works
    • Configure parameters for differential privacy
    • Perform differentially private data analysis
  • Module 11: Explain machine learning models with Azure Machine Learning

    • Learn how to explain models by calculating and interpreting feature importance.

  • Module 12: Detect and mitigate unfairness in models with Azure Machine Learning

    • In this module, you will learn:

    • How to evaluate machine learning models for fairness.
    • How to mitigate predictive disparity in a machine learning model.
  • Module 13: Monitor models with Azure Machine Learning

    • Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.

  • Module 14: Monitor data drift with Azure Machine Learning

    • Learn how to monitor data drift in Azure Machine Learning.

  • Module 15: Explore and experiment with securing a machine learning environment to ensure data remains private and models are accurate.

    • In this module, you will:

    • Apply and understand Role-Based Access Control in Azure Machine Learning
    • Describe how secrets are managed in Azure Machine Learning
    • Use an Azure Machine Learning workspace with Azure Virtual Network

Syllabus

  • Module 1: Introduction to the Azure Machine Learning SDK
    • Introduction
    • Azure Machine Learning workspaces
    • Exercise - Create a workspace
    • Azure Machine Learning tools and interfaces
    • Azure Machine Learning experiments
    • Exercise - Run experiments
    • Knowledge check
    • Summary
  • Module 2: Train a machine learning model with Azure Machine Learning
    • Introduction
    • Run a training script
    • Using script parameters
    • Registering models
    • Exercise - Training and registering a model
    • Knowledge check
    • Summary
  • Module 3: Work with Data in Azure Machine Learning
    • Introduction
    • Introduction to datastores
    • Use datastores
    • Introduction to datasets
    • Use datasets
    • Exercise - Work with data
    • Knowledge check
    • Summary
  • Module 4: Work with Compute in Azure Machine Learning
    • Introduction
    • Introduction to environments
    • Introduction to compute targets
    • Create compute targets
    • Use compute targets
    • Exercise - Work with Compute Contexts
    • Knowledge check
    • Summary
  • Module 5: Orchestrate machine learning with pipelines
    • Introduction
    • Introduction to pipelines
    • Pass data between pipeline steps
    • Reuse pipeline steps
    • Publish pipelines
    • Use pipeline parameters
    • Schedule pipelines
    • Exercise - Create a pipeline
    • Knowledge check
    • Summary
  • Module 6: Deploy real-time machine learning services with Azure Machine Learning
    • Introduction
    • Deploy a model as a real-time service
    • Consume a real-time inferencing service
    • Troubleshoot service deployment
    • Exercise - Deploy a model as a real-time service
    • Knowledge check
    • Summary
  • Module 7: Deploy batch inference pipelines with Azure Machine Learning
    • Introduction
    • Creating a batch inference pipeline
    • Publishing a batch inference pipeline
    • Exercise - Create a batch inference pipeline
    • Knowledge check
    • Summary
  • Module 8: Tune hyperparameters with Azure Machine Learning
    • Introduction
    • Defining a search space
    • Configuring sampling
    • Configuring early termination
    • Running a hyperparameter tuning experiment
    • Exercise - Tune hyperparameters
    • Knowledge check
    • Summary
  • Module 9: Automate machine learning model selection with Azure Machine Learning
    • Introduction
    • Automated machine learning tasks and algorithms
    • Preprocessing and featurization
    • Running automated machine learning experiments
    • Exercise - Using automated machine learning
    • Knowledge check
    • Summary
  • Module 10: Explore differential privacy
    • Introduction
    • Understand differential privacy
    • Configure data privacy parameters
    • Exercise - Use differential privacy
    • Knowledge check
    • Summary
  • Module 11: Explain machine learning models with Azure Machine Learning
    • Introduction
    • Feature importance
    • Using explainers
    • Creating explanations
    • Visualizing explanations
    • Exercise - Interpret models
    • Knowledge check
    • Summary
  • Module 12: Detect and mitigate unfairness in models with Azure Machine Learning
    • Introduction
    • Consider model fairness
    • Analyze model fairness with Fairlearn
    • Mitigate unfairness with Fairlearn
    • Exercise - Use Fairlearn with Azure Machine Learning
    • Knowledge check
    • Summary
  • Module 13: Monitor models with Azure Machine Learning
    • Introduction
    • Enable Application Insights
    • Capture and view telemetry
    • Exercise - Monitor a model
    • Knowledge check
    • Summary
  • Module 14: Monitor data drift with Azure Machine Learning
    • Introduction
    • Creating a data drift monitor
    • Scheduling alerts
    • Exercise - Monitor data drift
    • Knowledge check
    • Summary
  • Module 15: Explore security concepts in Azure Machine Learning
    • Introduction
    • What is role-based access control?
    • Exercise – Setup Azure Machine Learning with custom roles
    • Keys and secrets with Azure Key Vault
    • Secure your Azure Machine Learning network
    • Exercise - Setup a secure Azure Virtual Network
    • Knowledge check
    • Summary

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