Book Image

Applied Machine Learning and High-Performance Computing on AWS

By : Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter
Book Image

Applied Machine Learning and High-Performance Computing on AWS

By: Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

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.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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Conventions used
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1
Part 1: Introducing High-Performance Computing
Part 1: Introducing High-Performance Computing
Free Chapter
2
Chapter 1: High-Performance Computing Fundamentals
Chapter 1: High-Performance Computing Fundamentals
Why do we need HPC?
Limitations of on-premises HPC
Benefits of doing HPC on the cloud
Driving innovation across industries with HPC
Summary
Further reading
3
Chapter 2: Data Management and Transfer
Chapter 2: Data Management and Transfer
Importance of data management
Challenges of moving data into the cloud
How to securely transfer large amounts of data into the cloud
AWS online data transfer services
AWS offline data transfer services
Summary
Further reading
4
Chapter 3: Compute and Networking
Chapter 3: Compute and Networking
Introducing the AWS compute ecosystem
Networking on AWS
Selecting the right compute for HPC workloads
Best practices for HPC workloads
Summary
References
5
Chapter 4: Data Storage
Chapter 4: Data Storage
Technical requirements
AWS services for storing data
Data security and governance
Tiered storage for cost optimization
Choosing the right storage option for HPC workloads
Summary
Further reading
6
Part 2: Applied Modeling
Part 2: Applied Modeling
7
Chapter 5: Data Analysis
Chapter 5: Data Analysis
Technical requirements
Exploring data analysis methods
Reviewing the AWS services for data analysis
Analyzing large amounts of structured and unstructured data
Processing data at scale on AWS
Cleaning up
Summary
8
Chapter 6: Distributed Training of Machine Learning Models
Chapter 6: Distributed Training of Machine Learning Models
Technical requirements
Building ML systems using AWS
Introducing the fundamentals of distributed training
Executing a distributed training workload on AWS
Summary
9
Chapter 7: Deploying Machine Learning Models at Scale
Chapter 7: Deploying Machine Learning Models at Scale
Managed deployment on AWS
Choosing the right deployment option
Batch inference
Real-time inference
Asynchronous inference
The high availability of model endpoints
Blue/green deployments
Summary
References
10
Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment
Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment
Technical requirements
Understanding edge computing
Reviewing the key considerations for optimal edge deployments
Designing an architecture for optimal edge deployments
Summary
11
Chapter 9: Performance Optimization for Real-Time Inference
Chapter 9: Performance Optimization for Real-Time Inference
Technical requirements
Reducing the memory footprint of DL models
Key metrics for optimizing models
Choosing the instance type, load testing, and performance tuning for models
Observing the results
Summary
12
Chapter 10: Data Visualization
Chapter 10: Data Visualization
Data visualization using Amazon SageMaker Data Wrangler
Amazon’s graphics-optimized instances
Summary
Further reading
13
Part 3: Driving Innovation Across Industries
Part 3: Driving Innovation Across Industries
14
Chapter 11: Computational Fluid Dynamics
Chapter 11: Computational Fluid Dynamics
Technical requirements
Introducing CFD
Reviewing best practices for running CFD on AWS
Discussing how ML can be applied to CFD
Summary
References
15
Chapter 12: Genomics
Chapter 12: Genomics
Technical requirements
Managing large genomics data on AWS
Designing architecture for genomics
Applying ML to genomics
Summary
16
Chapter 13: Autonomous Vehicles
Chapter 13: Autonomous Vehicles
Technical requirements
Introducing AV systems
AWS services supporting AV systems
Designing an architecture for AV systems
ML applied to AV systems
Summary
References
17
Chapter 14: Numerical Optimization
Chapter 14: Numerical Optimization
Introduction to optimization
Common numerical optimization algorithms
Example use cases of large-scale numerical optimization problems
Numerical optimization using high-performance compute on AWS
Machine learning and numerical optimization
Summary
Further reading
18
Index
Index
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19
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Summary

In this chapter, we discussed numerical optimization and its applications. We started with a discussion about numerical optimization and its necessary ingredients. Next, we discussed a few of the common numerical optimization methods. We also discussed a few large-scale applications and use cases of numerical optimization. These use cases are very well known in academia as well as in the industry and are implemented by several organizations in their businesses. In addition, we talked about how AWS high-performance compute options and resources can be used to solve numerical optimization methods, and also discussed a few architectural patterns to accomplish this.

Finally, we ended with a short discussion about how various categories of machine learning algorithms employ numerical optimization at their core to build good models. The topics covered in this chapter will help you understand and formulate numerical optimization use cases, how numerical optimization is important...

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