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AWS for Solutions Architects

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4.3 (62)
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AWS for Solutions Architects

AWS for Solutions Architects

4.3 (62)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

The second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures. Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions. Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

Summary

Organizations must drive innovation and stay agile by using emerging technologies to stay ahead of the competition. With cloud providers like AWS, these technologies are easily accessible for you to experiment with and add to your use case.

In this chapter, you began by learning about ML and AI. You learned how AWS services help build an end-to-end ML pipeline, taking an ML workload from ideation to production. You learned about three layers of AWS AI/ML services, starting with the ML infrastructure provided by AWS to train your model.

After that, you learned about Amazon SageMaker, which is at the center of the AWS ML tech stack to build, train, deploy, tune, and monitor ML models. Next, you learned about the top stack where AWS AI services reside, providing pre-trained models that can be used with simple API calls without any knowledge of ML. This AI service is available to address multiple use cases involving vision, speech, chatbots, forecasting, and recommendations...

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