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Microsoft

Train compute-intensive models with Azure Machine Learning

Microsoft via Microsoft Learn

Overview

  • Module 1: Preprocess large datasets with Azure Machine Learning

    In this module, you'll learn how to:

    • Know when to choose CPU or GPU compute in Azure Machine Learning.
    • Efficiently store data in an Azure Data Lake Storage Gen2.
    • Optimize data loading and preprocessing.
  • Module 2: Train compute-intensive models with Azure Machine Learning

    In this module, you'll learn:

    • How to train a model with GPUs in Azure Machine Learning.
    • When to use which GPU option.
    • How to distribute model training.
  • Module 3: Deploy deep learning workloads to production with Azure Machine Learning

    In this module, you'll learn:

    • To choose the appropriate inference strategy
    • To optimize model scoring with ONNX
    • To deploy Triton as a managed online endpoint

Syllabus

  • Module 1: Module 1: Preprocess large datasets with Azure Machine Learning
    • Introduction
    • Choose the appropriate AI approach for your data
    • Efficient data storage options
    • Optimize data loading and preprocessing in Azure Machine Learning
    • Exercise: Preprocess data with RAPIDs
    • Knowledge check
    • Summary
  • Module 2: Module 2: Train compute-intensive models with Azure Machine Learning
    • Introduction
    • Train compute-intensive models with Azure Machine Learning
    • Choose the appropriate Nvidia GPU option
    • Exercise: Train a deep learning model with Azure Machine Learning
    • Distributed training with Azure Machine Learning
    • Knowledge check
    • Summary
  • Module 3: Module 3: Deploy deep learning workloads to production with Azure Machine Learning
    • Introduction
    • Choose the appropriate inference strategy
    • Standardize model formats and scale model deployment with ONNX and Triton
    • Exercise: Deploy an ONNX model with Triton in Azure Machine Learning
    • Knowledge check
    • Summary

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