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

Length: 374 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-01-14
ISBN-10: 1801811784
ISBN-13: 9781801811781
Sales Rank: #933329 (See Top 100 Books)

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.

Introduction to the Data-Driven Edge with Machine Learning
Foundations of Edge Workloads
Building the Edge
Extending the Cloud to the Edge
Ingesting and Streaming Data from the Edge
Processing and Consuming Data on the Cloud
Machine Learning Workloads at the Edge
DevOps and MLOps for the Edge
Fleet Management at Scale
Reviewing the Solution with AWS Well-Architected Framework

Intelligent Workloads at the Edge
Contributors
About the authors
About the reviewers
Preface
    Who this book is for
    What this book covers
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
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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