ai-engineering-hub README

The project's README from github.com/patchy631/ai-engineering-hub. As one markdown file: README.md.

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AI Engineering Hub 🚀

Welcome to the AI Engineering Hub - your comprehensive resource for learning and building with AI!

🌟 Why This Repo?

AI Engineering is advancing rapidly, and staying at the forefront requires both deep understanding and hands-on experience. Here, you will find:

Whether you're a beginner, practitioner, or researcher, this repo provides resources for all skill levels to experiment and succeed in AI engineering.


📋 Table of Contents


🎯 Getting Started

New to AI Engineering? Start here:

  1. Complete Beginners: Check out the AI Engineering Roadmap for a comprehensive learning path
  2. Learn the Basics: Start with Beginner Projects like OCR apps and simple RAG implementations
  3. Build Your Skills: Move to Intermediate Projects with agents and complex workflows
  4. Master Advanced Concepts: Tackle Advanced Projects including fine-tuning and production systems

📬 Stay Updated with Our Newsletter!

Get a FREE Data Science eBook 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. Subscribe now!

Daily Dose of Data Science Newsletter


🎓 Projects by Difficulty

🟢 Beginner Projects

Perfect for getting started with AI engineering. These projects focus on single components and straightforward implementations.

OCR & Vision

Chat Interfaces & UI

Basic RAG

Multimodal & Media

Other Tools


🟡 Intermediate Projects

Multi-component systems, agentic workflows, and advanced features for experienced practitioners.

AI Agents & Workflows

Voice & Audio

Advanced RAG

Multimodal

MCP (Model Context Protocol)

Model Comparison & Evaluation


🔴 Advanced Projects

Complex systems, fine-tuning, production deployments, and cutting-edge implementations.

Fine-tuning & Model Development

Advanced Agent Systems

Advanced MCP & Infrastructure

Production Systems

Learning Resources


📢 Contribute to the AI Engineering Hub!

We welcome contributors! Whether you want to add new tutorials, improve existing code, or report issues, your contributions make this community thrive. Here's how to get involved:

  1. Fork the repository
  2. Create a new branch for your contribution
  3. Submit a Pull Request and describe the improvements

Check out our contributing guidelines for more details.


📜 License

This repository is licensed under the MIT License - see the LICENSE file for details.


💬 Connect

For discussions, suggestions, and more, feel free to create an issue or reach out directly!

Happy Coding! 🎉

ai-engineering-hub (patchy631/ai-engineering-hub)

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这是什么?这是一个精选合集,包含 93 个以上的教程和真实项目实现,涵盖大语言模型(LLM)、RAG 系统和 AI 智能体,并按难度等级分类。
这是单个项目,还是多个独立教程的合集?这是一个合集:每个条目都是独立的 Jupyter Notebook 项目,演示某项特定的技术或工具,并分为初级、中级和高级三个部分。
初学者应该从哪里开始?初学者应先看 AI Engineering Roadmap(AI 工程路线图),再做 OCR 应用和基础 RAG 实现等简单项目,然后才学习基于智能体的中级项目。
所有项目都需要 API 密钥或云端模型吗?不需要。许多项目完全使用本地模型运行,例如通过 Ollama 运行的 Llama 3.2、Gemma-3、DeepSeek-R1 和 Qwen;其他项目则根据具体情况使用 Groq、SambaNova 或 NVIDIA NIM 等云服务。
每个项目是否都有自己的说明,可以独立使用?顶层 README 中没有明确说明。列出的每个项目很可能都有自己的笔记本文件和安装要求,但这些内容没有在仓库根目录中描述。
这个合集面向哪些人?面向希望获得动手实践、可灵活改造的代码示例,而非纯理论的 AI 工程领域初学者、从业者和研究人员。
MCP 部分涵盖哪些主题?包括:带深度网页搜索的自定义 MCP、为 Cursor 打造的 MCP 驱动 Agentic RAG、MCP 视频 RAG、结合 Firecrawl 和 Supabase 的语音智能体,以及使用 KitOps 进行机器学习模型管理的项目。
有没有直接对比不同大语言模型的项目?有。模型对比部分包含多个项目:Llama 4 对比 DeepSeek-R1、Qwen3 对比 DeepSeek-R1、O3 对比 Claude Code,以及 GPT-OSS 对比 Qwen3,比较它们在编程和推理等任务上的表现。
包含哪些高级项目?高级项目涵盖:使用 Unsloth 微调模型、构建 DeepSeek-R1 等推理模型、从零开始实现 Transformers、多智能体深度研究助手,以及完整的 NotebookLM 克隆等生产级系统。
订阅新闻通讯有额外资源吗?有。订阅本项目的新闻通讯即可免费获得一本包含 150 多节课的数据科学电子书。
Creator
patchy631
Size
302.9 MB
Stars
38.4K
Updated
CommitOct 12
Age
1y
Commits · 90 Days