Turn real-world challenges into opportunities by implementing advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud tools Key Features Solve real-world business problems with hands-on examples of Gen AI applications on Google Cloud Learn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethics Build and implement Gen AI agents and workflows like RAG and NL2SQL using LangChain and Vertex AI Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI is transforming businesses, and its rapid enterprise adoption is creating a demand for developers to quickly build and deploy AI applications that deliver real value. This book, Building generative AI applications on Google Cloud, is designed to help developers bridge the gap between concept and implementation, enabling them to leverage LangChain and Google Cloud's enterprise-ready tools for scalable AI solutions. You'll start by exploring the fundamentals of large language models (LLMs) and how LangChain simplifies the development of AI workflows by connecting LLMs with external data and services. The book guides you through using key tools like the Gemini and PaLM 2 APIs, Vertex AI, and Vertex AI Search to create sophisticated, production-ready generative AI applications. You'll also dive deep into advanced techniques like Retrieval-Augmented Generation (RAG) and external memory layers to overcome the context limitations of LLMs. With practical patterns and real-world examples, this book provides everything you need to harness Google Cloud's AI ecosystem, making it easier to reduce time to market while ensuring enterprise scalability. By the end of this book, you'll have the expertise to build generative AI applications that are robust, scalable, and tailored to solve real-world business challenges.What you will learn Build enterprise-ready applications with LangChain and Google Cloud Navigate and select the right Google Cloud generative AI tools Apply modern design patterns for generative AI applications Plan and execute proof-of-concepts for enterprise AI solutions Gain hands-on experience with LangChain's and Google Cloud's AI products Implement advanced techniques for text generation and summarization Leverage Vertex AI Search and other tools for scalable AI solutions Who this book is forIf you're an application developer or ML engineer eager to dive into generative AI, this book is for you. Whether you're new to LangChain or Google Cloud, you'll learn how to use these tools to build scalable AI solutions. This book is ideal for developers familiar with Python and machine learning basics, looking to apply their skills in generative AI. It's also a great fit for professionals who want to explore Google Cloud's powerful suite of enterprise-grade generative AI products and learn how to implement them effectively.

About the Author
Leonid Kuligin is an active contributor to LangChain and the author of the many Google Cloud integrations on LangChain. He has been building complex tech products for almost 20 years, developing intense data and Machine Learning applications. Leonid has a degree in Applied Mathematics and Physics, and a degree in Finance. Since 2023, he has been working with key Google Cloud customers on various generative AI initiatives. Jorge Zaldivar is an AI Engineer at Google and also a contributor to LangChain's integrations with Google. He has a decade of experience building complex Machine Learning applications and products applied to the energy and financial industries. Dr. Maximilian Tschochohei leads AI Engineering at Google Cloud Consulting EMEA and is responsible for implementing AI applications for Google Cloud customers. He brings insights into LangChain Before joining Google, he spent many years of experience in strategy and technology consulting with the Boston Consulting Group.


Book Information
ISBN 9781835889329
Author Leonid Kuligin
Format Paperback
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited