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Intelligent Workloads at the Edge Book Review
I received a copy of Packt's Intelligent Workloads at the Edge book, authored by Natu Lauchande, which can be found here: https://www.amazon.com/Intelligent-Workloads-Edge-cyber-physical-Greengrass/dp/1801811784.
Thanks to Indraneel Mitra who is the book co-author and a Principal Solutions Architect for IoT at Amazon Web Services, Ryan Burke who is a the book co-author and is a senior sustainability application architect at AWS, formerly the Worldwide Technical Leader.
With the introduction of concepts and examples, the authors' approach is particularly didactic. Another noteworthy characteristic is that, although being a technical read that necessitates the analysis and execution of code, the book can be read fast because the concepts are given objectively.
Thanks also for Abeer Dawe and Packt team.
The goal of this book is to discuss prescriptive patterns for developing end-to-end (E2E) solutions that run at the edge, which is the place in the computing topology closest to where analogue meets digital and vice versa.
The book concentrates on the edge use cases where machine learning (ML) technologies add the greatest value, and it shows you how to build these solutions using modern Amazon Web Services capabilities.
The book itself is very well structured, divided in 4 sections and 10 chapters. Section 1 is introductory, starting with a data-driven edge with ML.
Section 2 uses AWS technologies such as IoT Greengrass and Amazon SageMaker to build a solution that uses machine learning models to analyse data from local devices.
The following chapters make up this section:
- 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
The third section discusses how to deploy IoT fleets at scale and how to use DevOps methods to generate flexible and operationally efficient IoT workloads at scale utilising various well-architected practises.
The following chapters make up this section:
- For the Edge, DevOps and MLOps
- Large-Scale Fleet Management
Section 4 wraps up the hands-on project with a summary of what makes a solution well-architected (security, reliability, operational excellence, cost optimization, and performance).
Original Link: https://dev.to/aws-heroes/intelligent-workloads-at-the-edge-book-review-4d85
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