Train and Deploy a Machine Learning Model with Azure Machine Learning

Machine Learning Professionals Machine Learning Engineers

Description

 

  • Make data available in Azure Machine Learning
  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning
  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs
  • Register an MLflow model in Azure Machine Learning
  • Deploy a model to a managed online endpoint

Course Outline

 

Module 1: Make data available in Azure Machine Learning

  • Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.

 

Module 2 : Work with compute targets in Azure Machine Learning

  • Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

 

Module 3: Work with environments in Azure Machine Learning

  • Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

 

Module 4: Run a training script as a command job in Azure Machine Learning

  • Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.

 

Module 5: Track model training with MLflow in jobs

  • Learn how to track model training with MLflow in jobs when running scripts.

 

Module 6: Register an MLflow model in Azure Machine Learning

  • Learn how to log and register an MLflow model in Azure Machine Learning.

 

Module 7: Deploy a model to a managed online endpoint

  • Learn how to deploy models to a managed online endpoint for real-time inferencing.

 

 

 

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