Intelligent Workloads at the Edge: Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass

Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMaker

Key Features

• Accelerate your next edge-focused product development with the power of AWS IoT Greengrass
• Develop proficiency in architecting resilient solutions for the edge with proven best practices
• Harness the power of analytics and machine learning for solving cyber-physical problems

Book Description

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs.

This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance.

By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.

What you will learn

• Build an end-to-end IoT solution from the edge to the cloud
• Design and deploy multi-faceted intelligent solutions on the edge
• Process data at the edge through analytics and ML
• Package and optimize models for the edge using Amazon SageMaker
• Implement MLOps and DevOps for operating an edge-based solution
• Onboard and manage fleets of edge devices at scale
• Review edge-based workloads against industry best practices

Who this book is for

This book is for IoT architects and software engineers responsible for delivering analytical and machine learning–backed software solutions to the edge. AWS customers who want to learn and build IoT solutions will find this book useful. Intermediate-level experience with running Python software on Linux is required to make the most of this book.

Table of Contents

1. Introduction to the Data-Driven Edge with Machine Learning
2. Foundations of Edge Workloads
3. Building the Edge
4. Extending the Cloud to the Edge
5. Ingesting and Streaming Data from the Edge
6. Processing and Consuming Data on the Cloud
7. Machine Learning Workloads at the Edge
8. DevOps and MLOps for the Edge
9. Fleet Management at Scale
10. Reviewing the Solution with AWS Well-Architected Framework