Most LLM tutorials stop at a Jupyter notebook. This book goes further, focused on designing, training, and deploying large language models as production systems using MLOps practices rather than one-off experiments.
What it covers
Data engineering fundamentals, fine-tuning through supervised learning, and the deployment pipeline, illustrated through a practical example project (an LLM Twin) that ties the MLOps components together. It also goes into output optimization, preference alignment, and real-time data processing.
Who it's for
AI engineers, NLP specialists, and anyone working with language models who wants to go from experimentation to systems that run reliably with low latency and high availability. A basic grounding in LLMs, generative AI, Python, and AWS is recommended before starting.
You will explore the fundamental aspects of data engineering, fine-tuning using supervised learning, and the deployment process. Practical examples, such as creating a LLM Twin, will help you implement key MLOps components into your own projects. The book also covers advanced technologies in output optimization, preference alignment, and real-time data processing, making it an indispensable resource for engineers working with language models.
By the end of the reading, you will have mastered the skills for deploying LLMs capable of solving practical tasks with minimal latency and high availability. This book will be useful for both beginner AI specialists and experienced practitioners looking to deepen their knowledge and skills.
Who is this book for?
The book is intended for AI engineers, natural language processing specialists, and LLM engineers looking to deepen their knowledge of language models. A basic understanding of LLMs, generative AI, Python, and AWS is recommended. Regardless of your level of preparation, you will receive comprehensive guidance on applying LLMs in real-world scenarios.
What you will learn:
- Implement robust data pipelines and manage LLM training cycles
- Create your own LLMs and optimize them through practical examples
- Master the basics of LLMOps through key concepts such as orchestrators and prompt monitoring
- Perform supervised fine-tuning and model evaluation
- Deploy comprehensive LLM-based solutions using AWS and other tools
- Design scalable and modular LLM systems
- Explore the application of Retrieval-Augmented Generation (RAG) by building functions and data output pipelines
