LLMs in Production From language models to successful products

Learn how to put Large Language Model-based applications into production safely and efficiently.
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.
InLLMs in Productionyou will:
Grasp the fundamentals of LLMs and the technology behind themEvaluate when to use a premade LLM and when to build your ownEfficiently scale up an ML platform to handle the needs of LLMsTrain LLM foundation models and finetune an existing LLMDeploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRABuild applications leveraging the strengths of LLMs while mitigating their weaknesses
LLMs in Productiondelivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you'll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security.
Foreword by Joe Reis.
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 Productionteaches 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 insideBalancing cost and performanceRetraining and load testingOptimizing models for commodity hardwareDeploying on a Kubernetes cluster
About the reader
For data scientists and ML engineers who know Python and the basics of cloud deployment.
About the author
Christopher BrousseauandMatt Sharpare experienced engineers who have led numerous successful large scale LLM deployments.
Table of Contents
1 Words' awakening: Why large language models have captured attention
2 Large language models: A deep dive into language modeling
3 Large language model operations: Building a platform for LLMs
4 Data engineering for large language models: Setting up for success
5 Training large language models: How to generate the generator
6 Large language model services: A practical guide
7 Prompt engineering: Becoming an LLM whisperer
8 Large language model applications: Building an interactive experience
9 Creating an LLM project: Reimplementing Llama 3
10 Creating a coding copilot project: This would have helped you...



LLMs in Production From language models to successful products Fast Download
LLMs in Production From language models to successful products Full Download