AI-Infra-from-Zero-to-Hero

HuaizhengZhang/AI-Infra-from-Zero-to-Hero

🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice

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MITLast pushed Jul 25, 2025

Overview

A comprehensive collection of resources, papers, and industry practices related to machine learning systems, large language models (LLMs), and generative AI. The repository is organized into categories such as ML/DL infrastructure, LLM infrastructure, and domain-specific infrastructures, including video tutorials and links to recent conference proceedings.

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git clone https://github.com/HuaizhengZhang/AI-Infra-from-Zero-to-Hero

README

AI System School

💫💫💫 System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI)

Updates:

Path to System for AI [Whitepaper You Must Read]

A curated list of research in machine learning systems. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.

System for AI (Ordered by Category)

ML / DL Infra

  • Data Processing
  • Training System
  • Inference System
  • Machine Learning Infrastructure

LLM Infra

  • LLM Training
  • LLM Serving

Domain-Specific Infra

  • Video System
  • AutoML System
  • Edge AI
  • GNN System
  • Federated Learning System
  • Deep Reinforcement Learning System

System for ML/LLM Conference

Conference

  • OSDI
  • SOSP
  • SIGCOMM
  • NSDI
  • MLSys
  • ATC
  • Eurosys
  • Middleware
  • SoCC
  • TinyML

General Resources

  • Survey
  • Book
  • Video
  • Course
  • Blog

Survey

  • Toward Highly Available, Intelligent Cloud and ML Systems [Slide]
  • A curated list of awesome System Designing articles, videos and resources for distributed computing, AKA Big Data. [GitHub]
  • awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning [GitHub]
  • Opportunities and Challenges Of Machine Learning Accelerators In Production [Paper]
    • Ananthanarayanan, Rajagopal, et al. "
    • 2019 {USENIX} Conference on Operational Machine Learning (OpML 19). 2019.
  • How (and How Not) to Write a Good Systems Paper [Advice]
  • Applied machine learning at Facebook: a datacenter infrastructure perspective [Paper]
    • Hazelwood, Kim, et al. (HPCA 2018)
  • Infrastructure for Usable Machine Learning: The Stanford DAWN Project
    • Bailis, Peter, Kunle Olukotun, Christopher Ré, and Matei Zaharia. (preprint 2017)
  • Hidden technical debt in machine learning systems [Paper]
    • Sculley, David, et al. (NIPS 2015)
  • End-to-end arguments in system design [Paper]
    • Saltzer, Jerome H., David P. Reed, and David D. Clark.
  • System Design for Large Scale Machine Learning [Thesis]
  • Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications [Paper]
    • Park, Jongsoo, Maxim Naumov, Protonu Basu et al. arXiv 2018
    • Summary: This paper presents a characterizations of DL models and then shows the new design principle of DL hardware.
  • A Berkeley View of Systems Challenges for AI [[Paper]](https://arxiv.org/pdf/