Overview
About me
Hello, my name is Nikita Goryachev, and I am a Senior AI/ML Engineer at Sber. My team implementing SOTA algorithms in Natural Language Processing (NLP) and Recommender Systems (RecSys). We also organizing industry meetups, participating in prestigious conferences like the RecSys conference in Singapore and AI Journey in Moscow, and contributing to the development of RePlay, an expansive open-source library for recommender systems.
About This Book
I have authored a guide that serves as an essential resource for Data Scientists, ML Engineers, Software Developers, and other professionals who find themselves navigating the complex landscape of modern artificial intelligence technologies. This book is crafted to demystify the complexities inherent in AI applications, with a focused lens on Large Language Models (LLMs), conversational AI, and the nuanced process of integrating LLMs into development workflows, particularly emphasizing the tailored application of Machine Learning Operations (ML Ops) for these models. My goal is to equip readers with the knowledge and tools needed to harness the potential of AI technologies, guiding them through the intricacies of this rapidly evolving field with clarity and insight.
The methodologies and insights provided in this book extend beyond theoretical understanding, illustrating how LLMs can be seamlessly integrated into various business scenarios. From enhancing customer service with conversational chatbots to personalizing user experiences through recommender systems, and optimizing operational efficiencies with ML Ops, the application of LLMs across different facets of a business is both broad and impactful. This guide emphasizes the fundamental application skills required to implement LLMs in any business process, highlighting the versatility and transformative potential of these models in driving innovation, improving decision-making, and creating value. By bridging the gap between complex AI technologies and practical business applications, this book equips professionals with the knowledge to not only navigate the landscape of artificial intelligence but to also leverage it as a powerful tool for business growth and innovation.
Chapter 1: Mastering the Essentials of OpenAI API
The chapter begins with an introduction to the ChatGPT API, setting the stage for a comprehensive exploration of its capabilities, classifications, and applications. This section is designed to familiarize readers with the foundational concepts and the significance of the ChatGPT technology in the current tech landscape. It outlines the purpose and scope of the chapter, providing a roadmap for understanding the intricate aspects of the ChatGPT API, including advanced moderation techniques, the enhancement of machine reasoning, the strategic use of prompt chaining, and the methodologies involved in building and evaluating large language model (LLM) applications. This introductory overview serves as a gateway for readers, offering them a clear context and preparing them for a deeper dive into the specifics of how ChatGPT and similar technologies are revolutionizing the way we interact with AI-driven systems.
Chapter 2: Creating Conversational Chatbots with LangChain
The second chapter transitions to the practicalities of developing conversational chatbots, utilizing LangChain to enhance interactive user interfaces. It covers the entire development process from environment setup to the implementation of advanced retrieval techniques. Special attention is given to incorporating conversational context and memory, signifying a leap towards more dynamic and human-like interactions. Through technical guidance and user-centric design principles, this chapter illustrates the journey towards creating engaging and meaningful conversational AI systems.
Chapter 3: ML Ops for LLMs a.k.a. LLMOps
Finally, Chapter 3 provides a structured guide for integrating LLMs into development workflows, with a focus on ML Ops practices tailored specifically for LLMs. It outlines the critical steps from model selection and tuning to deployment and monitoring, emphasizing the role of automation and best practices in managing and scaling LLM-based applications. Through practical guidance and case studies, this chapter aims to equip professionals with the skills to innovate and maintain efficient AI solutions, contributing to the advancement of intelligent applications and the ethical use of AI in development.
This book is not just a compilation of methodologies and techniques; it is a comprehensive manual designed to empower professionals to harness the potential of AI technologies responsibly and innovatively. The book addresses the technical, ethical, and practical aspects of AI development, offering a roadmap for those looking to advance in the rapidly evolving field of LLM Ops.