English | 2024 | ISBN: 9781633437203 | 338 pages | PDF,EPUB | 15.83 MB

Learn how to put Large Language Model-based applications into production safely and efficiently.

Large Language Models (LLMs) are the foundation of AI tools like ChatGPT, LLAMA and Bard. 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.

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 RLHF
Build applications leveraging the strengths of LLMs while mitigating their weaknesses

LLMs in Production delivers vital insights into delivering MLOps for LLMs. Youll learn how to operationalize these powerful AI models for chatbots, coding assistants, and more. 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.

about the book
LLMs in Production is the comprehensive guide to LLMs youll need to effectively guide one to production usage. It takes you through the entire lifecycle of an LLM, from initial concept, to creation and fine tuning, all the way to deployment. Youll discover how to effectively prepare an LLM dataset, cost-efficient training techniques like LORA and RLHF, and how to evaluate your models against industry benchmarks.

Learn to properly establish deployment infrastructure and address common challenges like retraining and load testing. Finally, youll go hands-on with three exciting example projects: a cloud-based LLM chatbot, a Code Completion VSCode Extension, and deploying LLM to edge devices like Raspberry Pi. By the time youre done reading, youll be ready to start developing LLMs and effectively incorporating them into software.
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about the reader
For data scientists and ML engineers, who know Python and the basics of Kubernetes and cloud deployment.

about the authors
Christopher Brousseau is a Principle Machine Learning Engineer with a linguistics and localization background. He specializes in linguistically-informed NLP, especially with an international focus and has led successful ML and Data product initiatives at both startups and Fortune 500s.

Matt Sharp is an engineer and seasoned technology leader in MLOps. Has led successful initiatives for both startups and top-tier tech companies. Matt specializes in deploying, managing, and scaling machine learning models.




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