Awesome System For Machine Learning

A curated list of research in machine learning systems (MLSys). Paper notes are also provided.
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Awesome System for Machine Learning

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Path to system for AI [Whitepaper You Must Read]

A curated list of research in machine learning system. 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.

AI system

General Resources

System for AI Papers/Opensource (Ordered by Category)

General System

Specific System

System for ML or ML for System on Top System Conference (with notes)



  • NIPS learning system workshop
  • ICML learning system workshop
  • OptML (Rising Star)
  • HotCloud
  • HotEdge
  • HotEdge
  • HotOS
  • NetAI (ACM SIGCOMM Workshop on Network Meets AI & ML)
  • EdgeSys (EuroSys workshop about edge computing) [website]
  • EuroMLSys [website]
  • Large-Scale Distributed Systems and Middleware (LADIS) (EuroSys workshop) [website]


  • 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]


  • Computer Architecture: A Quantitative Approach [Must read]
  • Streaming Systems [Book]
  • Kubernetes in Action (start to read) [Book]
  • Machine Learning Systems: Designs that scale [Website]
  • Trust in Machine Learning [Website]
  • Automated Machine Learning in Action [Website]


  • ScalaDML2020: Learn from the best minds in the machine learning community. [Video]
  • Jeff Dean: "Achieving Rapid Response Times in Large Online Services" Keynote - Velocity 2014 [YouTube]
  • From Research to Production with PyTorch [Video]
  • Introduction to Microservices, Docker, and Kubernetes [YouTube]
  • ICML Keynote: Lessons Learned from Helping 200,000 non-ML experts use ML [Video]
  • Adaptive & Multitask Learning Systems [Website]
  • System thinking. A TED talk. [YouTube]
  • Flexible systems are the next frontier of machine learning. Jeff Dean [YouTube]
  • Is It Time to Rewrite the Operating System in Rust? [YouTube]
  • InfoQ: AI, ML and Data Engineering [YouTube]
    • Start to watch.
  • Netflix: Human-centric Machine Learning Infrastructure [InfoQ]
  • SysML 2019: [YouTube]
  • ScaledML 2019: David Patterson, Ion Stoica, Dawn Song and so on [YouTube]
  • ScaledML 2018: Jeff Dean, Ion Stoica, Yangqing Jia and so on [YouTube] [Slides]
  • A New Golden Age for Computer Architecture History, Challenges, and Opportunities. David Patterson [YouTube]
  • How to Have a Bad Career. David Patterson (I am a big fan) [YouTube]
  • SysML 18: Perspectives and Challenges. Michael Jordan [YouTube]
  • SysML 18: Systems and Machine Learning Symbiosis. Jeff Dean [YouTube]
  • AutoML Basics: Automated Machine Learning in Action. Qingquan Song, Haifeng Jin, Xia Hu [YouTube]



  • Parallelizing across multiple CPU/GPUs to speed up deep learning inference at the edge [Amazon Blog]
  • Building Robust Production-Ready Deep Learning Vision Models in Minutes [Blog]
  • Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker [Blog]
  • How to Deploy a Machine Learning Model -- Creating a production-ready API using FastAPI + Uvicorn [Blog] [GitHub]
  • Deploying a Machine Learning Model as a REST API [Blog]
  • Continuous Delivery for Machine Learning [Blog]
  • Kubernetes CheatSheets In A4 [GitHub]
  • A Gentle Introduction to Kubernetes [Blog]
  • Train and Deploy Machine Learning Model With Web Interface - Docker, PyTorch & Flask [GitHub]
  • Learning Kubernetes, The Chinese Taoist Way [GitHub]
  • Data pipelines, Luigi, Airflow: everything you need to know [Blog]
  • The Deep Learning Toolset — An Overview [Blog]
  • Summary of CSE 599W: Systems for ML [Chinese Blog]
  • Polyaxon, Argo and Seldon for Model Training, Package and Deployment in Kubernetes [Blog]
  • Overview of the different approaches to putting Machine Learning (ML) models in production [Blog]
  • Being a Data Scientist does not make you a Software Engineer [Part1] Architecting a Machine Learning Pipeline [Part2]
  • Model Serving in PyTorch [Blog]
  • Machine learning in Netflix [Medium]
  • SciPy Conference Materials (slides, repo) [GitHub]
  • 继Spark之后,UC Berkeley 推出新一代AI计算引擎——Ray [Blog]
  • 了解/从事机器学习/深度学习系统相关的研究需要什么样的知识结构? [Zhihu]
  • Learn Kubernetes in Under 3 Hours: A Detailed Guide to Orchestrating Containers [Blog] [GitHub]
  • data-engineer-roadmap: Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups [GitHub]
  • TensorFlow Serving + Docker + Tornado机器学习模型生产级快速部署 [Blog]
  • Deploying a Machine Learning Model as a REST API [Blog]
  • Colossal-AI: A Unified Deep Learning System for Big Model Era [Blog] [GitHub]
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