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Ultimate Generative AI Solutions on Google Cloud [#1021819]
Ultimate Generative AI Solutions on Google Cloud: Practical Strategies for Building and Scaling Generative AI Solutions with Google Cloud Tools, Langchain, RAG, and LLMOps (English Edition) by Arun Pandey
English | January 13, 2025 | ISBN: 9348107127 | 334 pages | MOBI | 18 Mb
Unlock Generative AI's Potential: Transform Ideas into Reality on Google Cloud!
Book Description
Generative AI, powered by Google Cloud Platform (GCP), is reshaping industries with its advanced capabilities in automating and enhancing complex tasks. The Ultimate Generative AI Solutions on Google Cloud is your comprehensive guide to harnessing this powerful combination to innovate and excel in your job role. It explores foundational machine learning concepts and dives deep into Generative AI, providing the essential knowledge needed to conceptualize, develop, and deploy cutting-edge AI solutions.
Within these pages, you'll explore Large Language Models (LLMs), Prompt engineering, Fine-tuning techniques, and the latest advancements in AI, with special emphasis on Parameter-Efficient Fine-Tuning (PEFT) and Reinforcement Learning with Human Feedback (RLHF). You'll also learn about the integration of LangChain and Retrieval-Augmented Generation (RAG) to enhance AI capabilities. By mastering these techniques, you can optimize model performance while conserving resources. The integration of GCP services simplifies the development process, enabling the creation of robust AI applications with ease.
By the end of this book, you will not only understand the technical aspects of Generative AI but also gain practical skills that can transform your work to drive innovation and boost operational efficiency with Generative AI on GCP.
Table of Contents
1. Generative AI Essentials
2. Google Cloud Basics
3. Getting Started with Large Language Models
4. Prompt Engineering and Contextual Learning
5. Fine-Tuning a Large Language Model
6. Parameter-Efficient Fine-Tuning (PEFT)
7. Reinforcement Learning with Human Feedback
8. Model Optimization
9. LLMOps for Managing and Monitoring AI Projects
10. Harnessing RAG and LangChain
11. Case Studies and Real-World Implementations
Index
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