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Train and manage a machine learning model with Azure Machine Learning

Offered By: Microsoft via Microsoft Learn

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Azure Machine Learning Courses Data Management Courses Model Training Courses Machine Learning Model Deployment Courses MLFlow Courses

Course Description

Overview

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  • Module 1: Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets.

    In this module, you'll learn how to:

    • Work with Uniform Resource Identifiers (URIs).
    • Create and use datastores.
    • Create and use data assets.
  • Module 2: Azure Machine Learning Python SDK v2

    In this module, you'll learn how to:

    • Choose the appropriate compute target.
    • Create and use a compute instance.
    • Create and use a compute cluster.
  • Module 3: Use Azure Machine Learning Python SDK v2 to work with environments

    In this module, you'll learn how to:

    • Understand environments in Azure Machine Learning.
    • Explore and use curated environments.
    • Create and use custom environments.
  • Module 4: Run a command job with the Azure Machine Learning Python SDK v2.

    In this module, you'll learn how to:

    • Convert a notebook to a script.
    • Test scripts in a terminal.
    • Run a script as a command job.
    • Use parameters in a command job.
  • Module 5: Learn how to track model training with MLflow in jobs when running scripts.

    In this module, you learn how to:

    • Use MLflow when you run a script as a job.
    • Review metrics, parameters, artifacts, and models from a run.
  • Module 6: Azure Machine Learning Python SDK v2.

    In this module, you'll learn how to:

    • Log models with MLflow.
    • Understand the MLmodel format.
    • Register an MLflow model in Azure Machine Learning.
  • Module 7: Learn how to deploy models to a managed online endpoint for real-time inferencing.

    In this module, you'll learn how to:

    • Use managed online endpoints.
    • Deploy your MLflow model to a managed online endpoint.
    • Deploy a custom model to a managed online endpoint.
    • Test online endpoints.

Syllabus

  • Module 1: Module 1: Make data available in Azure Machine Learning
    • Introduction
    • Understand URIs
    • Create a datastore
    • Create a data asset
    • Exercise - Make data available in Azure Machine Learning
    • Knowledge check
    • Summary
  • Module 2: Module 2: Work with compute targets in Azure Machine Learning
    • Introduction
    • Choose the appropriate compute target
    • Create and use a compute instance
    • Create and use a compute cluster
    • Exercise - Work with compute resources
    • Knowledge check
    • Summary
  • Module 3: Module 3: Work with environments in Azure Machine Learning
    • Introduction
    • Understand environments
    • Explore and use curated environments
    • Create and use custom environments
    • Exercise - Work with environments
    • Knowledge check
    • Summary
  • Module 4: Module 4: Run a training script as a command job in Azure Machine Learning
    • Introduction
    • Convert a notebook to a script
    • Run a script as a command job
    • Use parameters in a command job
    • Exercise - Run a training script as a command job
    • Knowledge check
    • Summary
  • Module 5: Module 5: Track model training with MLflow in jobs
    • Introduction
    • Track metrics with MLflow
    • View metrics and evaluate models
    • Exercise - Use MLflow to track training jobs
    • Knowledge check
    • Summary
  • Module 6: Module 6: Register an MLflow model in Azure Machine Learning
    • Introduction
    • Log models with MLflow
    • Understand the MLflow model format
    • Register an MLflow model
    • Exercise - Log and register models with MLflow
    • Knowledge check
    • Summary
  • Module 7: Module 7: Deploy a model to a managed online endpoint
    • Introduction
    • Explore managed online endpoints
    • Deploy your MLflow model to a managed online endpoint
    • Deploy a model to a managed online endpoint
    • Test managed online endpoints
    • Exercise - Deploy an MLflow model to an online endpoint
    • Knowledge check
    • Summary

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