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Build and operate machine learning solutions with Azure Machine Learning

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Free Online Course: Build and operate machine learning solutions with Azure Machine Learning provided by Microsoft Learn is a comprehensive online course, which lasts for 10-11 hours worth of material. The course is taught in English and is free of charge.

Microsoft via Microsoft Learn
10-11 hours worth of material
Self Paced
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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