Awesome System for Machine Learning

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.

General Resources

• Survey
• Book
• Video
• Course
• Blog

System for AI Papers (Ordered by Category)

• Data Processing
• Training System
• Inference System
• Machine Learning Infrastructure
• AutoML System
• Deep Reinforcement Learning System
• Edge AI
• Video System
• GNN System
• Federated Learning System

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

Conferene

• MLsys [2021 notes]
• NSDI [2021 notes]
• OSDI [2020 notes]
• SOSP
• SIGCOMM
• ATC
• Eurosys [2021 notes]
• Middleware
• SoCC [2021 Notes]
• TinyML [2021]

Workshop

• 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]

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]

Book

• 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]

Video

• 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]

Course

• CS692 Seminar: Systems for Machine Learning, Machine Learning for Systems [GitHub]
• Topics in Networks: Machine Learning for Networking and Systems, Autumn 2019 [Course Website]
• CS6465: Emerging Cloud Technologies and Systems Challenges [Cornell]
• CS294: AI For Systems and Systems For AI. [UC Berkeley Spring] (Strong Recommendation) [Machine Learning Systems (Fall 2019)]
• CSE 599W: System for ML. [Chen Tianqi] [University of Washington]
• EECS 598: Systems for AI (W'21). [Mosharaf Chowdhury] [Systems for AI (W'21)]
• Tutorial code on how to build your own Deep Learning System in 2k Lines [GitHub]
• CSE 291F: Advanced Data Analytics and ML Systems. [UCSD]
• CSci 8980: Machine Learning in Computer Systems [University of Minnesota, Twin Cities]
• Mu Li (MxNet, Parameter Server): Introduction to Deep Learning [Best DL Course I think] [Book]
• 10-605: Machine Learning with Large Datasets. [CMU]
• CS 329S: Machine Learning Systems Design. [Stanford]

Blog

• 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]