LLMs in Production
From Language Models to Successful Products
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ISBN
9781633437203
Bindwijze
Hardcover
Taal
Engels
Auteur
Uitgeverij
Manning Publications
Jaar van uitgifte
2025
Aantal pagina's
400
Waar gaat het over?
Learn how to put Large Language Model-based applications into production safely and efficiently.
From the back cover: LLMs in Production is the comprehensive guide to LLMs you'll need to effectively guide you 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. You'll 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, you'll 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 you're done reading, you'll be ready to start developing LLMs and effectively incorporating them into software. About the reader: For data scientists and ML engineers, who know Python and the basics of Kubernetes and cloud deployment.
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
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