Large Language Models Recipes is a comprehensive guide designed to help developers, and AI practitioners navigate the complexities of working with LLMs. It explains fine-tuning open-source models and deploying scalable AI solutions, providing practical insights and hands-on examples in a recipe-style format for easy understanding and application.
The book begins with a step-by-step guide to setting up an efficient development environment, covering hardware considerations, cloud services, and essential tools like PyTorch and TensorFlow. It then introduces readers to open-source language models, offering guidance on selecting and loading models such as GPT-J, LLaMA, and Falcon. It has dedicated chapters exploring fine-tuning, transfer learning, and quantization techniques to optimize performance. Readers will also discover advanced topics, including model distillation, deployment strategies on cloud platforms like AWS and GCP, and efficient data handling methods. Additionally, it covers scaling down large models for limited-resource environments, monitoring and debugging techniques, and integrating external tools such as vector databases for Retrieval-Augmented Generation (RAG).
By the end of this book, readers will have a solid foundation in working with LLMs―from setting up their environment to deploying efficient, scalable AI solutions. With practical recipes, real-world applications, and cutting-edge techniques, Large Language Models Recipes is an essential resource for anyone looking to harness the full potential of LLMs in modern AI workflows.
What you will learn:
- How to configure hardware, install essential tools, and optimize workflows for working with LLMs.
- Explore techniques like fine-tuning, quantization, and model distillation for efficient performance.
- Explore deployment strategies, cloud platforms, and edge computing for real-world applications.
- Understand multimodal LLMs, Retrieval-Augmented Generation (RAG), and external tool integrations.
Who this book is for:
This book is ideal for data scientists, machine learning engineers, and AI enthusiasts looking to understand and develop Large Language Models and their applications.
Bharath Kumar Bolla is a highly accomplished Data Science leader with over 15 years of experience, specializing in AI, NLP, and Deep Learning for the past decade. He holds an M.S. in Data Science (The University of Arizona) and an Executive MBA (Product Management).
As an Associate Director at Novartis, he currently drives strategic MLOps initiatives, successfully designing and scaling automated pipelines and deploying cutting-edge Generative AI solutions across multiple European markets. His commercial impact is notable, including architecting a Salesforce recommendation system (5-10% conversion boost) and developing an ML pricing optimization product (+$1.2M revenue at Verizon).
Beyond corporate leadership, Bharath is a prolific academic with over 30 peer-reviewed publications and multiple best paper awards. He is recognized as a "40 Under 40 Data Scientist" (2022) and an "AI Changemaker," and actively supports the community by reviewing AI books and mentoring students.
Kalpa Subbaiah is a leading Data Scientist and AI expert with over 17 years of experience, including more than a decade in Data Science and Machine Learning. She holds a Master’s degree in Machine Learning and Artificial Intelligence from Liverpool John Moores University and specializes in building end-to-end AI solutions across Azure, Databricks, and AWS.
Kalpa is a GenAI expert, building production-grade LLM and RAG applications, fine-tuning models via Hugging Face, and architecting scalable multi-agent AI systems with robust evaluation frameworks. Her strong experience in finance and manufacturing drives projects in financial AI platforms, smart city solutions, and enterprise analytics, leveraging capabilities such as document intelligence and object detection.
As Vice President and Lead Data Scientist at JPMorgan Chase & Co., she drives large-scale AI/GenAI transformation across the enterprise. Highly certified (Azure Data Scientist/AI Engineer, AWS ML Specialist), she actively delivers global corporate and academic training as a technical trainer and mentor.
Sashi Kiran Kaata is a seasoned cloud data engineer, researcher, and technology leader with over 10 years of experience architecting large-scale data platforms and real-time analytics solutions. Holding a Master’s degree in Information Science and Technology, he has core expertise in AWS, Snowflake, Databricks, and modern streaming frameworks. At First Citizens Bank, he led enterprise data modernization, designing resilient ingestion and governance architectures, including Data Movement Controls (DMC), which significantly improved platform performance, reliability, and regulatory compliance.
Sashi blends practical engineering with research-driven innovation, actively contributing to the community as a technical conference speaker on distributed systems and scalable cloud architectures. His work extends into blockchain-based workflow modernization, sustainable AI pipelines, and adaptive ETL systems designed to support the next generation of intelligent data platforms. He is a prolific author of numerous peer-reviewed publications on critical areas, including cloud cost optimization, self-healing systems, Green AI, and MicroLLMs.
