9781803244440_

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.

Key Features

Understanding the need for High Performance Computing (HPC).
Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker.
Best practices and architectures for implementing ML at scale using HPC.

Book Description

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 like computational fluid dynamics (CFD), genomics, and autonomous vehicles.

The book provides end-to-end guidance starting from HPC concepts for storage and networking. It then goes deeper into part 2, with working examples on how to process large datasets using SageMaker Studio and EMR, build, train, and deploy large models using distributed training. It also covers 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 will be able to build, train, and deploy your own large scale ML application, using HPC on AWS, following the industry best practices and addressing the key pain points encountered in the application life cycle.

What you will learn

Data management, storage, and fast networking for HPC applications
Analysis and visualization of a large volume of data using Spark
Train visual transformer model using SageMaker distributed training
Deploy and manage ML models at scale on cloud and at edge
Performance optimization of ML models for low latency workloads
Apply HPC to industry domains like CFD, genomics, AV, and optimization

Who This Book Is For

The book begins with HPC concepts, however, expects you to have prior machine learning knowledge. This book is for ML engineers and Data Scientists, interested in learning advanced topics on using large dataset for training large models using distributed training concepts on AWS, followed by deploying models at scale and performance optimization for low latency use cases. This book is also beneficial for Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale.

High Performance Computing Fundamentals
Data Management and Transfer
Compute and Networking
Data Storage
Data Analysis
Distributed Training of Machine Learning Models
Deploying Machine Learning Models at Scale
Optimizing and Managing Machine Learning Models for Edge Deployment
Performance Optimization for Real-time Inference on Cloud
Visualization
Computational Fluid Dynamics
Genomics
Autonomous Vehicles
Numerical Optimization

...(*_*)9781803244440_

Key Features

Understand the need for high-performance computing (HPC)
Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker
Learn best practices and architectures for implementing ML at scale using HPC

Book Description

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, youll 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, youll 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.

What you will learn

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

Who this book is for

The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.

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Key Features

Book Description

What you will learn

Who This Book Is For

Table of Contents

Key Features

Book Description

What you will learn

Who this book is for

Applied Machine Learning and High-Performance Computing on AWS

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