PacktPublishing/LLM-Engineers-Handbook resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

5296 stars · 1285 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

The official companion repository for the book 'LLM Engineer's Handbook' by Paul Iusztin and Maxime Labonne, containing Python code for building an end-to-end LLM-based system. It covers data collection, LLM training, a RAG system, AWS deployment, monitoring, and evaluation following LLMOps best practices.

Use cases

• learn llm engineering from fundamentals to production
• build and deploy a rag application on aws
• fine-tune a llama model with dpo
• set up llm training and evaluation pipelines
• learn llmops best practices for monitoring and deployment
• study an end-to-end ml system design example

When to choose

• you are reading the LLM Engineer's Handbook and want the latest maintained code
• you want a hands-on, end-to-end project covering LLM training, RAG, and AWS deployment
• you want to learn LLMOps practices like monitoring, testing, and evaluation

When to avoid

• you need a production-ready library or framework to drop into your own project
• you want a tool without needing to follow the accompanying book
• you are not working in Python or on AWS

Facets

learning-resource · maturity active

llm-training rag machine-learning monitoring testing etl large-language-models machine-learning tutorials cloud-computing python cloud llmops mlops book-companion fine-tuning llm-evaluation aws-deployment hands-on-project retrieval-augmented-generation devops docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PacktPublishing/LLM-Engineers-Handbook")