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The Machine Learning Solutions Architect Handbook - Second Edition

You're reading from  The Machine Learning Solutions Architect Handbook - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Pages 602 pages
Edition 2nd Edition
Languages
Author (1):
David Ping David Ping
Profile icon David Ping

Table of Contents (19) Chapters

Preface Navigating the ML Lifecycle with ML Solutions Architecture Exploring ML Business Use Cases Exploring ML Algorithms Data Management for ML Exploring Open-Source ML Libraries Kubernetes Container Orchestration Infrastructure Management Open-Source ML Platforms Building a Data Science Environment Using AWS ML Services Designing an Enterprise ML Architecture with AWS ML Services Advanced ML Engineering Building ML Solutions with AWS AI Services AI Risk Management Bias, Explainability, Privacy, and Adversarial Attacks Charting the Course of Your ML Journey Navigating the Generative AI Project Lifecycle Designing Generative AI Platforms and Solutions Other Books You May Enjoy
Index

Bringing it all together

Having delved into the various technical components separately within the generative AI technical stack, let’s now consolidate them into a unified perspective.

Figure 16.8: Generative AI tech stack

In summary, a generative AI platform is an extension of an ML platform by introducing additional capabilities such as prompt management, input/output filtering, and tools for FM evaluation and RLHF workflows. To accommodate these enhancements, the ML platform’s pipeline capability will need to include new generative AI workflows. The new RAG infrastructure will form the foundational backbone of RAG-based LLM applications and will be closely integrated with the underlying generative AI platform.

The development of generative AI applications will continue to leverage other core application architecture components, including streaming, batch processing, message queuing, and workflow tools.

Although many of the core components will...

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