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

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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 Other Books You May Enjoy