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).

Format:

Book

Author/Creator:

Pandey, Arun.

Language:

English

Subjects (All):

Artificial intelligence.

Generative programming (Computer science).

Physical Description:

1 online resource (227 pages)

Edition:

1st ed.

Place of Publication:

Delhi : Orange Education PVT Ltd, 2024.

Summary:

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.

Contents:

Cover Page

Title Page

Copyright Page

Dedication Page

Foreword

About the Author

Technical Review Partner

Acknowledgements

Preface

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Errata

Table of Contents

1. Generative AI Essentials

Introduction

Structure

Introduction to Machine Learning

History and Evolution

Early Concepts and Foundations

The Emergence of Neural Networks

The Rise of Generative Adversarial Networks (GANs)

The Advent of Variational Autoencoders (VAEs)

Transformer-Based Models and Diffusion Models

Latest Developments and Applications

Ethical Considerations and Future Directions

Machine Learning Core Concepts

Data

Algorithms

Models

Training

Validation and Testing Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9789348107121

9348107127

OCLC:

1485004760