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Book Overview & Buying
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Table Of Contents
Applied Machine Learning and High-Performance Computing on AWS
By :
Applied Machine Learning and High-Performance Computing on AWS
By:
Overview of this book
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.
By the end of this 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.
Table of Contents (20 chapters)
Preface
Part 1: Introducing High-Performance Computing
Free Chapter
Chapter 1: High-Performance Computing Fundamentals
Chapter 2: Data Management and Transfer
Chapter 3: Compute and Networking
Chapter 4: Data Storage
Part 2: Applied Modeling
Chapter 5: Data Analysis
Chapter 6: Distributed Training of Machine Learning Models
Chapter 7: Deploying Machine Learning Models at Scale
Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment
Chapter 9: Performance Optimization for Real-Time Inference
Chapter 10: Data Visualization
Part 3: Driving Innovation Across Industries
Chapter 11: Computational Fluid Dynamics
Chapter 12: Genomics
Chapter 13: Autonomous Vehicles
Chapter 14: Numerical Optimization
Index
