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Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (59)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.7 (59)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Master the art of solution architecture and excel as a Solutions Architect with the Solutions Architect's Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead. Unlock the potential of cutting-edge technologies, gain practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.
Table of Contents (20 chapters)
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18
Other Books You May Enjoy
19
Index

Summary

In this chapter, we delved into the fascinating world of generative AI, starting with a comprehensive exploration of what it is. We explored the diverse use cases that generative AI enables, from transforming customer experiences to enhancing employee productivity and optimizing various aspects of business operations.

To understand the basic architecture of generative AI systems, we broke down the different types of generative models, including GANs, VAEs, and transformer-based models. We highlighted the importance of hyperparameter tuning and regularization in constructing effective generative AI architectures.

In the context of FMs, we provided insights into some of the popular generative AI FMs offered by key players in the field, such as Amazon, OpenAI, Google, Nvidia, and several others. These models serve as the backbone of generative AI applications.

For those eager to start their journey with generative AI, we offered guidance tailored to different user...

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