- 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
Train compute-intensive models with Azure Machine Learning
Overview
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
Tags
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