LLMs in Production: From language models to successful products

Christopher Brousseau, Matthew Sharp

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.

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

LLMs in Production delivers 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.

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.