09-05-2026, 01:49 PM
LLMs in Production: From Language Models to Successful Products
Authors: Christopher Brousseau & Matthew Sharp
Publisher: Manning Publications
Publication: February 11, 2025 (trade paperback)
Length: 456 pages
LLMs in Production is a practical guide to moving large language models from experimentation into reliable, real-world applications. Rather than concentrating only on transformer theory or training models from scratch, Brousseau and Sharp focus on the engineering and operational problems that appear once an LLM has to serve actual users. The book introduces the fundamentals of LLMs, dataset preparation, model selection, prompt-based interaction, evaluation, fine-tuning, and the broader idea of LLMOps—the processes required to develop, deploy, monitor, retrain, and maintain language-model systems.
A major theme is deciding how much of the model stack you really need to own. The authors compare using existing foundation models with training or adapting your own, and explain techniques such as LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and RLHF (Reinforcement Learning from Human Feedback). They also address practical trade-offs involving computational cost, latency, model quality, infrastructure, scalability, benchmarking, and deployment. The emphasis is that a successful LLM product is not simply a good model: it requires an entire production pipeline around that model.
The book is strongly project-oriented. Readers work through examples including training a custom language model, creating an AI-powered VS Code extension, and deploying a small model on a Raspberry Pi. It also discusses cloud deployment, Kubernetes, commodity and edge hardware, load testing, and optimization. This makes the book particularly suitable for ML engineers, data scientists, and developers who already know Python and basic cloud deployment and want to progress from experimenting with LLM APIs toward building maintainable AI systems.
Key takeaways
BOOK
Authors: Christopher Brousseau & Matthew Sharp
Publisher: Manning Publications
Publication: February 11, 2025 (trade paperback)
Length: 456 pages
LLMs in Production is a practical guide to moving large language models from experimentation into reliable, real-world applications. Rather than concentrating only on transformer theory or training models from scratch, Brousseau and Sharp focus on the engineering and operational problems that appear once an LLM has to serve actual users. The book introduces the fundamentals of LLMs, dataset preparation, model selection, prompt-based interaction, evaluation, fine-tuning, and the broader idea of LLMOps—the processes required to develop, deploy, monitor, retrain, and maintain language-model systems.
A major theme is deciding how much of the model stack you really need to own. The authors compare using existing foundation models with training or adapting your own, and explain techniques such as LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and RLHF (Reinforcement Learning from Human Feedback). They also address practical trade-offs involving computational cost, latency, model quality, infrastructure, scalability, benchmarking, and deployment. The emphasis is that a successful LLM product is not simply a good model: it requires an entire production pipeline around that model.
The book is strongly project-oriented. Readers work through examples including training a custom language model, creating an AI-powered VS Code extension, and deploying a small model on a Raspberry Pi. It also discusses cloud deployment, Kubernetes, commodity and edge hardware, load testing, and optimization. This makes the book particularly suitable for ML engineers, data scientists, and developers who already know Python and basic cloud deployment and want to progress from experimenting with LLM APIs toward building maintainable AI systems.
Key takeaways
- LLM engineering is much more than prompt engineering: production systems need evaluation, infrastructure, monitoring, deployment, and lifecycle management.
- Fine-tuning does not always require retraining everything. Techniques such as LoRA and PEFT can adapt large models far more economically.
- Cost, latency, reliability, and model quality must be optimized together rather than treating accuracy as the only objective.
- The book bridges the gap between understanding LLMs and actually turning them into deployable products, making it especially useful as an introduction to LLMOps and production AI engineering.
BOOK
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