Train compute-intensive models 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.

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.

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

• 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

• 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

• 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