--- title: "LLM-Engineers-Handbook" type: "tool" slug: "packtpublishing-llm-engineers-handbook" canonical_url: "https://www.graphcanon.com/tools/packtpublishing-llm-engineers-handbook" github_url: "https://github.com/PacktPublishing/LLM-Engineers-Handbook" homepage_url: "https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/" stars: 5160 forks: 1240 primary_language: "Python" license: "MIT" categories: ["model-training", "inference-serving", "evaluation-observability"] tags: ["llmops", "genai", "ml-system-design", "fine-tuning-llm", "aws", "rag", "mlops", "llm-evaluation"] updated_at: "2026-07-07T18:40:20.417535+00:00" --- # LLM-Engineers-Handbook > LLM Engineer's Handbook by Paul Iusztin and Maxime Labonne A repository for the book 'The LLM’s Practical Guide', which focuses on deploying advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) applications to AWS. ## Facts - Repository: https://github.com/PacktPublishing/LLM-Engineers-Handbook - Homepage: https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/ - Stars: 5,160 · Forks: 1,240 · Open issues: 34 · Watchers: 60 - Primary language: Python - License: MIT - Last pushed: 2026-04-22T08:25:03+00:00 ## Categories - [Model Training](/categories/model-training.md) - [Inference & Serving](/categories/inference-serving.md) - [Evaluation & Observability](/categories/evaluation-observability.md) ## Tags llmops, genai, ml-system-design, fine-tuning-llm, aws, rag, mlops, llm-evaluation ## Related tools - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) - [vllm](/tools/vllm-project-vllm.md) - A high-throughput and memory-efficient inference and serving engine for LLMs (★ 85,612) - [rtk](/tools/rtk-ai-rtk.md) - CLI proxy reducing LLM token consumption by 60-90% (★ 69,253) - [unsloth](/tools/unslothai-unsloth.md) - Unsloth Studio is a web UI for training and running open models locally. (★ 67,883) - [anything-llm](/tools/mintplex-labs-anything-llm.md) - Stop renting your intelligence. Own it with AnythingLLM. (★ 62,759) ## README (excerpt) ```text

👷 LLM Engineer's Handbook

Official repository of the LLM Engineer's Handbook by Paul Iusztin and Maxime Labonne

Find the book on Amazon or Packt

## 🌟 Features The goal of this book is to create your own end-to-end LLM-based system using best practices: - 📝 Data collection & generation - 🔄 LLM training pipeline - 📊 Simple RAG system - 🚀 Production-ready AWS deployment - 🔍 Comprehensive monitoring - 🧪 Testing and evaluation framework You can download and use the final trained model on [Hugging Face](https://huggingface.co/mlabonne/TwinLlama-3.1-8B-DPO). > [!IMPORTANT] > The code in this GitHub repository is actively maintained and may contain updates not reflected in the book. **Always refer to this repository for the latest version of the code.** ## 🔗 Dependencies ### Local dependencies To install and run the project locally, you need the following dependencies. | Tool | Version | Purpose | Installation Link | |------|---------|---------|------------------| | pyenv | ≥2.3.36 | Multiple Python versions (optional) | [Install Guide](https://github.com/pyenv/pyenv?tab=readme-ov-file#installation) | | Python | 3.11 | Runtime environment | [Download](https://www.python.org/downloads/) | | Poetry | >= 1.8.3 and < 2.0 | Package management | [Install Guide](https://python-poetry.org/docs/#installation) | | Docker | ≥27.1.1 | Containerization | [Install Guide](https://docs.docker.com/engine/install/) | | AWS CLI | ≥2.15.42 | Cloud management | [Install Guide](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) | | Git | ≥2.44.0 | Version control | [Download](https://git-scm.com/downloads) | ### Cloud services The code also uses and depends on the following cloud services. For now, you don't have to do anything. We will guide you in the installation and deployment sections on how to use them: | Service | Purpose | |---------|---------| | [HuggingFace](https://huggingface.com/) | Model registry | | [Comet ML](https://www.comet.com/site/products/opik/?utm_source=llm_handbook&utm_medium=github&utm_campaign=opik) | Experiment tracker | | [Opik](https://www.comet.com/site/products/opik/?utm_source=llm_handbook&utm_medium=github&utm_campaign=opik) | Prompt monitoring | | [ZenML](https://www.zenml.io/) | Orchestrator and artifacts layer | | [AWS](https://aws.amazon.com/) | Compute and storage | | [MongoDB](https://www.mongodb.com/) | NoSQL database | | [Qdrant](https://qdrant.tech/) | Vector database | | [GitHub Actions](https://github.com/features/actions) | CI/CD pipeline | In the [LLM Engineer's Handbook](https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/), Chapter 2 will walk you through each tool. Chapters 10 and 11 provide step-by-step guides on how to set up everything you need. ## 🗂️ Project Structure Here is the directory overview: ```bash . ├── code_snippets/ # Standalone exampl ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/packtpublishing-llm-engineers-handbook`](/api/graphcanon/tools/packtpublishing-llm-engineers-handbook) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_