Applied Machine Learning and High-Performance Computing on AWS

Applied Machine Learning and High-Performance Computing on AWS

eBook Details:

  • Paperback: 382 pages
  • Publisher: WOW! eBook (December 30, 2022)
  • Language: English
  • ISBN-10: 1803237015
  • ISBN-13: 978-1803237015

eBook Description:

Applied Machine Learning and High-Performance Computing on AWS: Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.

This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.

  • Explore data management, storage, and fast networking for HPC applications
  • Focus on the analysis and visualization of a large volume of data using Spark
  • Train visual transformer models using SageMaker distributed training
  • Deploy and manage ML models at scale on the cloud and at the edge
  • Get to grips with performance optimization of ML models for low latency workloads
  • Apply HPC to industry domains such as CFD, genomics, AV, and optimization

By the end of this Applied Machine Learning and High-Performance Computing on AWS book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

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