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 an accomplished data scientist, researcher, and mentor with over 12 years of expertise in statistical analysis, machine learning, and natural language processing. Named among the "40 Under 40 Data Scientists" by Analytics India Magazine, he has successfully led teams in developing AI-driven solutions across the telecom, healthcare, marketing, and ed-tech sectors.
His work includes building advanced recommendation systems, optimizing pricing models that delivered multimillion-dollar ROIs, and pioneering sentiment analysis tools. With over twenty peer-reviewed publications in computer vision, forecasting, and NLP, Bharath is also a dedicated academic. He holds advanced degrees in data science, applied statistics, and life sciences from leading institutions. A passionate advocate for continuous learning, he frequently speaks at industry forums and serves as a technical reviewer for leading AI publications. His expertise in deep learning frameworks, statistical modelling, and scalable AI solutions positions him as a thought leader and innovator in the field.
Kalpa Subbaiah is a seasoned data scientist and AI expert with over 16 years of experience, including 8 years in data science and machine learning. Holding a Master’s in Machine Learning and AI from Liverpool John Moores University, she specializes in deploying end-to-end AI solutions on Azure Machine Learning, Databricks, and AWS. Her expertise spans computer vision, NLP, and deep learning frameworks like TensorFlow, PyTorch, and Keras. A certified Azure Data Scientist, AI Engineer, and AWS ML Specialist, Kalpa has designed scalable AI pipelines, MLOps solutions, and cutting-edge projects in sentiment analysis, object detection, and smart city solutions.
As a technical trainer and mentor, she delivers corporate and academic training worldwide, contributing through blogs, workshops, and community engagements. Currently, as Vice President and Lead Data Scientist at JPMorgan Chase & Co., she spearheads AI/ML initiatives, driving innovation and strategic AI advancements.
