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End-to-end machine learning operations (MLOps) with Azure Machine Learning

Offered By: Microsoft via Microsoft Learn

Tags

MLOps Courses DevOps Courses GitHub Actions Courses YAML Courses Unit Testing Courses Azure Machine Learning Courses Model Deployment Courses

Course Description

Overview

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  • Module 1: MLOps, machine learning operations

    In this module, you'll learn how to:

    • Convert notebook to scripts.
    • Work with YAML to define a command or pipeline job.
    • Run scripts as a job with the CLI v2.
  • Module 2: MLOps, machine learning operations

    In this module, you'll learn how to:

    • Create and assign a service principal the permissions needed to run an Azure Machine Learning job.
    • Store Azure credentials securely using secrets in GitHub Secrets.
    • Create a GitHub Action using YAML that uses the stored Azure credentials to run an Azure Machine Learning job.
  • Module 3: Learn how to trigger GitHub Actions with feature-based development to achieve machine learning operations or MLOps.

    In this module, you'll learn how to:

    • Work with feature-based development.
    • Protect the main branch.
    • Trigger a GitHub Actions workflow by merging a pull request.
  • Module 4: machine learning operations, MLOps, linter, linting, unit tests, code check

    In this module, you'll learn how to:

    • Run linters and unit tests with GitHub Actions.
    • Integrate code checks with pull requests.
    • Troubleshoot errors to improve your code.
  • Module 5: machine learning operations, MLOps, environments

    In this module, you'll learn how to:

    • Set up environments in GitHub.
    • Use environments in GitHub Actions.
    • Add approval gates to assign required reviewers before moving the model to the next environment.
  • Module 6: machine learning operations, MLOps, model deployment, online endpoint

    In this module, you'll learn how to:

    • Deploy a model to a managed endpoint.
    • Trigger model deployment with GitHub Actions.
    • Test the deployed model.

Syllabus

  • Module 1: Module 1: Use an Azure Machine Learning job for automation
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Create Azure Machine Learning jobs
    • Exercise
    • Knowledge check
    • Summary
  • Module 2: Module 2: Trigger Azure Machine Learning jobs with GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Use GitHub Actions for model training
    • Exercise
    • Knowledge check
    • Summary
  • Module 3: Module 3: Trigger GitHub Actions with feature-based development
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Trigger a workflow
    • Exercise
    • Knowledge check
    • Summary
  • Module 4: Module 4: Work with linting and unit testing in GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Run linting and unit testing
    • Exercise
    • Knowledge check
    • Summary
  • Module 5: Module 5: Work with environments in GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Set up environments
    • Exercise
    • Knowledge check
    • Summary
  • Module 6: Module 6: Deploy a model with GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Model deployment
    • Exercise
    • Knowledge check
    • Summary

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