Table of Contents

Preface

Section 1: Introduction and Prerequisites

Chapter 1: Introduction to the Data-Driven Edge with Machine Learning

Living on the edge

Common concepts for edge solutions

Bringing ML to the edge

Tools to get the job done

Edge runtime

ML

Communicating with the edge

Demand for smart home and industrial IoT

Smart home use cases

Industrial use cases

Setting the scene: A modern smart home solution

Hands-on prerequisites

System 1: The edge device

System 2: Command and control (C2)

Summary

Knowledge check

References

Section 2: Building Blocks

Chapter 2: Foundations of Edge Workloads

Technical requirements

The anatomy of an edge ML solution

Designing code for business logic

Physical interfaces

Network interfaces

IoT Greengrass for the win

Reviewing IoT Greengrass architecture

Checking compatibility with IoT Device Tester

Booting the Raspberry Pi

Configuring the AWS account and permissions

Configuring IDT

Installing IoT Greengrass

Reviewing what has been created so far

Creating your first edge component

Reviewing an existing component

Writing your first component

Summary

Knowledge check

References

Chapter 3: Building the Edge

Technical requirements

Exploring the topology of the edge

Reviewing common standards and protocols

IoT Greengrass in the OSI model

IoT Greengrass in ANSI/ISA-95

Application layer protocols

Message format protocols

Security at the edge

End devices to your gateway

The gateway device

Edge components

Connecting your first device – sensing at the edge

Installing the sensor component

Reviewing the sensor component

Connecting your second device – actuating at the edge

Installing the component

Reviewing the actuator component

Summary

Knowledge check

References

Chapter 4: Extending the Cloud to the Edge

Technical requirements

Creating and deploying remotely

Loading resources from the cloud

Packaging your components for remote deployment

Storing logs in the cloud

Merging component configuration

Synchronizing the state between the edge and the cloud

Introduction to device shadows

Steps to deploy components for state synchronization

Extending the managed components

Deploying your first ML model

Reviewing the ML use case

Steps to deploy the ML workload

Summary

Knowledge check

References

Chapter 5: Ingesting and Streaming Data from the Edge

Technical requirements

Defining data models for IoT workloads

What is data management?

What is data modeling?

How do you design data models for IoT?

Selecting between ACID or BASE for IoT workloads

Conceptual modeling of the connected HBS hub

The logical modeling of the connected HBS hub

The physical modeling of the connected HBS hub

Designing data patterns on the edge

Data storage

Data integration concepts

Data flow patterns

Data flow anti-patterns for the edge

A hands-on approach with the lab

Building cloud resources

Building edge components

Validating the data streamed from the edge to the cloud

Additional topics for reference

Time series databases

Unstructured data

Summary

Knowledge check

References

Chapter 6: Processing and Consuming Data on the Cloud

Technical requirements

Defining big data for IoT workloads

What is big data processing?

What is domain-driven design?

What are the principles to design data workflows using DDD?

Designing data patterns on the cloud

Data storage

Data integration patterns

Data flow patterns

Data flow anti-patterns for the cloud

A hands-on approach with the lab

Building cloud resources

Querying the ODS

Building the analytics workflow

Summary

Knowledge check

References

Chapter 7: Machine Learning Workloads at the Edge

Technical requirements

Defining ML for IoT workloads

What is the history of ML?

What are the different types of ML systems?

Taxonomy of ML with IoT workloads

Why is ML accessible at the edge today?

Designing an ML workflow in the cloud

Business understanding and problem framing

Data collection or integration

Data preparation

Data visualization and analytics

Feature engineering (FE)

Model training

Model evaluation and deployment

ML design principles

ML anti-patterns for IoT workloads

Hands-on with ML architecture

Building the ML workflow

Deploying the model from cloud to the edge

Performing ML inferencing on the edge and validating results

Summary

Knowledge check

References

Section 3: Scaling It Up

Chapter 8: DevOps and MLOps for the Edge

Technical requirements

Defining DevOps for IoT workloads

Fundamentals of DevOps

Relevance of DevOps for IoT and the edge

DevOps challenges with IoT workloads

Understanding the DevOps toolchain for the edge

AWS Lambda at the edge

Containers for the edge

Additional toolsets for Greengrass deployments

MLOps at the edge

Relevance of MLOps for IoT and the edge

MLOps challenges for the edge

Understanding the MLOps toolchain for the edge

Hands-on with the DevOps architecture

Deploying the container from the cloud to the edge

Summary

Knowledge check

References

Chapter 9: Fleet Management at Scale

Technical requirements

Onboarding a fleet of devices globally

Registering a certificate authority

Deciding the provisioning approach

Managing your device fleet at scale

Monitor

Maintenance

Diagnose

Getting hands-on with Fleet Hub architecture

Building the cloud resources

Deploying the components from the cloud to the edge

Visualizing the results

Summary

Knowledge check

References

Section 4: Bring It All Together

Chapter 10: Reviewing the Solution with AWS Well-Architected Framework

Summarizing the key lessons

Defining edge ML solutions

Using IoT Greengrass

Modeling data and ML workloads

Operating a production solution

Describing the AWS Well-Architected Framework

Reviewing the solution

Reflecting upon the solution

Applying the framework

Diving deeper into AWS services

AWS IoT Greengrass

AWS IoT services

Machine learning services

Ideas for further proficiency

Summary

References

Appendix 1 – Answer Key

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

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