Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Solutions Architect's Handbook
  • Toc
  • feedback
Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (59)
close
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)
close
18
Other Books You May Enjoy
19
Index

The basic architecture of generative AI systems

At the heart of generative AI systems is a massive FM. FMS are large-scale, pre-trained models that have been trained on vast datasets and can be fine-tuned or adapted for a wide range of tasks and applications. To understand the architecture of generative AI systems, let’s break it down into simple components:

  • Generator: The core element that generates new data, whether it’s images, text, music, or other forms of content. The generator learns patterns and relationships from existing data and uses this knowledge to produce new, similar content. For example, the generator takes random noise in image generation and produces images that resemble the training data.
  • Latent space: A conceptual space where the model represents data in a compressed form. It’s like a compact representation of the data that the generator uses to create new content. This is a lower-dimensional vector space from which the generator...
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete