Book Image

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4 (2)
Book Image

AWS for Solutions Architects - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Imagine crafting cloud solutions that are secure, scalable, and optimized – not just good, but industry-leading. This updated guide throws open the doors to the AWS Well-Architected Framework, design pillars, and cloud-native design patterns empowering you to craft secure, performant, and cost-effective cloud architectures. Tame the complexities of networking, conquering edge deployments and crafting seamless hybrid cloud connections. Uncover the secrets of big data and streaming with EMR, Glue, Kinesis, and MSK, extracting valuable insights from data at speeds you never thought possible. Future-proof your cloud with game-changing insights! New chapters unveil CloudOps, machine learning, IoT, and blockchain, empowering you to build transformative solutions. Plus, unlock the secrets of storage mastery, container excellence, and data lake patterns. From simple configurations to sophisticated architectures, this guide equips you with the knowledge to solve any cloud challenge and impress even the most demanding clients. This book is your one-stop shop for architecting industry-standard AWS solutions. Stop settling for average – dive in and build like a pro!
Table of Contents (19 chapters)
17
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18
Index

AI/ML in AWS

In recent years, ML has rapidly transitioned from a cutting-edge technology to a mainstream one; however, there is still a long way to go before ML is embedded everywhere in our lives. In the past, ML was primarily accessible to a select group of large tech companies and academic researchers. But with the advent of cloud computing, the resources required to work with ML, such as computing power and data, have become more widely available, enabling a wider range of organizations to utilize and benefit from ML technology.

ML has become an essential technology for many industries, and AWS is at the forefront of providing ML services to its customers. Some of the key trends in ML using AWS include:

  • Serverless ML: AWS is making it easier to build, train, and deploy ML models without the need to manage servers. With services like Amazon SageMaker, customers can build and train models using managed Jupyter Notebook and then deploy them to a serverless endpoint...