LLMs in production : from language models to successful products / Christopher Brousseau, Matthew Sharp ; foreword by Joe Reis.
| Author/creator | Brousseau, Christopher author. |
| Other author | Sharp, Matt (Engineer), author. |
| Other author | Reis, Joe (Joseph), writer of foreword. |
| Format | Book |
| Publication | Shelter Island, NY : Manning Publications Co., [2025] |
| Copyright Date | ©2025 |
| Description | xx, 432 pages : illustrations ; 23 cm |
| Subjects |
| Variant title | Large Language Models in production |
| Contents | Words' awakening: why large language models have captured attention -- Large language models: a deep dive into language modeling -- Large language model operations: building a platform for LLMs -- Data engineering for large language models: setting up for success -- Training large language models: how to generate the generator -- Large language model services: a practical guide -- Prompt engineering: becoming an LLM whisperer -- Large language model applications: building an interactive experience -- Creating an LLM project: reimplementing Llama 3 -- Creating a coding copilot project: this would have helped you earlier -- Deploying an LLM on a Raspberry Pi: How low can you go? -- Production, an ever-changing landscape: Things are just getting started. |
| Abstract | 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. 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. |
| General note | Includes index. |
| Spec. audience char. | For data scientists and ML engineers who know Python and the basics of cloud deployment |
| ISBN | 1633437205 |
| ISBN | 9781633437203 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Joyner | General Stacks | QA76.9 .N38 B76 2025 | ✔ Available | Place Hold |