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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
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19
Index

Challenges in implementing generative AI

Implementing generative AI, while highly promising, comes with its set of challenges and considerations. In the following subsections, we delve into some of the primary challenges associated with generative AI.

Training stability issues

One of the significant challenges encountered in generative AI is training stability issues. These issues can manifest as convergence problems, slow training, or even divergence, making it difficult to obtain high-quality generative models.

One prevalent application of generative AI involves using a GAN to create high-definition images. Training stability issues may arise during the training of a GAN for image generation. For instance, the generator may produce nonsensical or highly distorted images. These issues can hinder the GAN from converging to a satisfactory solution, leading to poor image generation quality.

Addressing and preventing training stability issues in generative AI involves...

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