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

Cover
Title page
Copyright and Credits
Contributors
About reviewers
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
Index
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