1 Words’ awakening: Why large language models have captured attention 1
2 Large language models: A deep dive into language modeling 20
3 Large language model operations: Building a platform for LLMs 73
4 Data engineering for large language models: Setting up for success 111
5 Training large language models: How to generate the generator 154
6 Large language model services: A practical guide 201
7 Prompt engineering: Becoming an LLM whisperer 254
8 Large language model applications: Building an interactive experience 279
9 Creating an LLM project: Reimplementing Llama 3 305
10 Creating a coding copilot project: This would have helped you earlier 332
11 Deploying an LLM on a Raspberry Pi: How low can you go? 355
12 Production, an ever-changing landscape: Things are just getting started 379

 This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice.

 In LLMs in Production you will:

• Grasp the fundamentals of LLMs and the technology behind them
• Evaluate when to use a premade LLM and when to build your own
• Efficiently scale up an ML platform to handle the needs of LLMs
• Train LLM foundation models and finetune an existing LLM
• Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA
• Build applications leveraging the strengths of LLMs while mitigating their weaknesses

About the technology

 Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands.

About the book

 LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi.

What's inside

• Balancing cost and performance
• Retraining and load testing
• Optimizing models for commodity hardware
• Deploying on a Kubernetes cluster

About the reader

 For data scientists and ML engineers who know Python and the basics of cloud deployment.