Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural be

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

• 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, 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.

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.

Table of Contents

1. High-Performance Computing Fundamentals
2. Data Management and Transfer
3. Compute and Networking
4. Data Storage
5. Data Analysis
6. Distributed Training of Machine Learning Models
7. Deploying Machine Learning Models at Scale
8. Optimizing and Managing Machine Learning Models for Edge Deployment
9. Performance Optimization for Real-Time Inference
10. Data Visualization
11. Computational Fluid Dynamics
12. Genomics
13. Autonomous Vehicles
14. Numerical Optimization

Mani Khanuja is a seasoned IT professional with over 17 years of software engineering experience. She has successfully led machine learning and artificial intelligence projects in various domains, such as forecasting, computer vision, and natural language processing. At AWS, she helps customers to build, train, and deploy large machine learning models at scale. She also specializes in data preparation, distributed model training, performance optimization, machine learning at the edge, and automating the complete machine learning life cycle to build repeatable and scalable applications.

Farooq Sabir is a research and development expert in machine learning, data science, big data, predictive analytics, computer vision, and image and video processing. He has over 10 years of professional experience.

Shreyas Subramanian helps AWS customers build and fine-tune large-scale machine learning and deep learning models, and rearchitect solutions to help improve the security, scalability, and efficiency of machine learning platforms. He also specializes in setting up massively parallel distributed training, hyperparameter optimization, and reinforcement learning solutions, and provides reusable architecture templates to solve AI and optimization use cases.

Trenton Potgieter is an expert technologist with 25 years of both local and international experience across multiple aspects of an organization; from IT to sales, engineering, and consulting, on the cloud and on-premises. He has a proven ability to analyze, assess, recommend, and design appropriate solutions that meet key business criteria, as well as present and teach them from engineering to executive levels.