Detailed Introduction
AI Engineering Hub is an open-source curated collection maintained by the author, focused on translating large model techniques into production-ready engineering practices. It aggregates in-depth tutorials, runnable examples, and templates covering large language models (LLMs), retrieval-augmented generation (RAG), MLOps, deployment and agentic applications, helping engineers convert conceptual methods into reproducible workflows.
Main Features
- Hands-on tutorials: step-by-step guides and example projects for LLMs, RAG and multimodal setups.
- Engineering-first: emphasis on reproducible code, deployment, and evaluation pipelines for production use.
- Open and extendable: MIT-licensed and community-contributable for new examples and tooling.
- Broad coverage: includes data pipelines, prompt engineering, fine-tuning, and monitoring practices.
Use Cases
This hub suits developers learning production AI engineering, product teams building prototypes, and SRE/engineering teams establishing model deployment workflows. The examples and best practices accelerate iteration from experiment to production.
Technical Features
The repository primarily uses Python and Jupyter Notebooks, providing modular examples, experiment artifacts, and deployment notes. It emphasizes integration with standard toolchains (containerization, CI/CD, observability) and includes runnable demos for fast validation of engineering hypotheses.