/awesome-mlops

A curated list of references for MLOps

Awesome MLOps Awesome Made With Love

MLOps. You Desing It. Your Train It. You Run It.

An awesome list of references for MLOps - Machine Learning Operations.

Table of Content
MLOps Core MLOps Communities
MLOps Books MLOps Articles
MLOps Papers Talks About MLOps
Existing ML Systems Machine Learning
Software Engineering Product Management for ML/AI
The Economics of ML/AI Model Governance, Ethics, Responsible AI

MLOps Core

  1. Machine Learning Operations: You Train It, You Run It!
  2. MLOps SIG Specification
  3. ML in Production
  4. Awesome production machine learning: State of MLOps Tools and Frameworks
  5. Udemy “Deployment of ML Models”
  6. Full Stack Deep Learning

MLOps Communities

  1. MLOps.community
  2. CDF Special Interest Group - MLOps

MLOps Books

  1. Book “Machine Learning Engineering” by Andriy Burkov, 2020
  2. Book: "ML Ops: Operationalizing Data Science" by David Sweenor, Steven Hillion, Dan Rope, Dev Kannabiran, Thomas Hill, Michael O'Connell
  3. Book "Building Machine Learning Powered Applications" by Emmanuel Ameisen
  4. Book "Building Machine Learning Pipelines" – O’Reilly
  5. Book "Managing Data Science" by Kirill Dubovikov
  6. Book "Accelerated DevOps with AI, ML & RPA: Non-Programmer's Guide to AIOPS & MLOPS" by Stephen Fleming
  7. Book "Evaluating Machine Learning Models" by Alice Zheng
  8. Book: Agile AI. 2020. By Carlo Appugliese, Paco Nathan, William S. Roberts. O'Reilly Media, Inc.
  9. Book: "Machine Learning Logistics". 2017. By T. Dunning et al. O'Reilly Media Inc.

MLOps Articles

  1. Continuous Delivery for Machine Learning (by Thoughtworks)
  2. Linux Foundation AI Foundation
  3. MLSpec: A project to standardize the intercomponent schemas for a multi-stage ML Pipeline.
  4. State of Enterprise ML 2019: PDF
  5. State of Enterprise ML 2019: Interactive
  6. Organizing machine learning projects: project management guidelines.
  7. Rules for ML Project (Best practices)
  8. ML Pipeline Template
  9. Data Science Project Structure
  10. Reproducible ML
  11. ML project template facilitating both research and production phases.
  12. Machine learning requires a fundamentally different deployment approach. As organizations embrace machine learning, the need for new deployment tools and strategies grows.
  13. Efficient ML engineering: Tools and best practices
  14. Why is DevOps for Machine Learning so Different?
  15. Lessons learned turning machine learning models into real products and services – O’Reilly
  16. MLOps: Model management, deployment and monitoring with Azure Machine Learning
  17. Guide to File Formats for Machine Learning: Columnar, Training, Inferencing, and the Feature Store
  18. Architecting a Machine Learning Pipeline How to build scalable Machine Learning systems
  19. Why Machine Learning Models Degrade In Production
  20. Concept Drift and Model Decay in Machine Learning
  21. Bringing ML to Production
  22. A Tour of End-to-End Machine Learning Platforms
  23. What Does it Mean to Deploy a Machine Learning Model?
  24. Software Interfaces for Machine Learning Deployment
  25. Batch Inference for Machine Learning Deployment
  26. MLOps: Continuous delivery and automation pipelines in machine learning
  27. AI meets operations
  28. What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps
  29. Forbes: The Emergence Of ML Ops
  30. Cognilytica Report "ML Model Management and Operations 2020 (MLOps)"
  31. Introducing Cloud AI Platform Pipelines
  32. A Guide to Production Level Deep Learning
  33. The 5 Components Towards Building Production-Ready Machine Learning Systems
  34. Deep Learning in Production (references about deploying deep learning-based models in production)
  35. Machine Learning Experiment Tracking
  36. The Team Data Science Process (TDSP)
  37. MLOps Solutions (Azure based)
  38. Monitoring ML pipelines
  39. Deployment & Explainability of Machine Learning COVID-19 Solutions at Scale with Seldon Core and Alibi
  40. Demystifying AI Infrastructure
  41. Monitoring Machine Learning Models in Production
  42. Organizing machine learning projects: project management guidelines.
  43. The Checklist for Machine Learning Projects (from Aurélien Géron,"Hands-On Machine Learning with Scikit-Learn and TensorFlow")
  44. Data Project Checklist by Jeremy Howard
  45. MLOps: not as Boring as it Sounds
  46. 10 Steps to Making Machine Learning Operational. Cloudera White Paper
  47. AI Infrastructure for Everyone: DeterminedAI
  48. MLOps is Not Enough. The Need for an End-to-End Data Science Lifecycle Process.
  49. Data Science Lifecycle Repository Template
  50. Template: code and pipeline definition for a machine learning project demonstrating how to automate an end to end ML/AI workflow.
  51. Nitpicking Machine Learning Technical Debt
  52. The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups
  53. Software Engineering for AI/ML - An Annotated Bibliography
  54. Intelligent System. Machine Learning in Practice
  55. CMU 17-445/645: Software Engineering for AI-Enabled Systems (SE4AI)
  56. Machine Learning is Requirements Engineering
  57. Machine Learning Reproducibility Checklist
  58. Why We Need DevOps for ML Data
  59. Machine Learning Ops. A collection of resources on how to facilitate Machine Learning Ops with GitHub.
  60. CI/CD for Machine Learning & AI
  61. Data Preparation for Machine Learning (7-Day Mini-Course)
  62. AWS Cost Optimization for ML Infrastructure - EC2 spend
  63. Task Cheatsheet for Almost Every Machine Learning Project A checklist of tasks for building End-to-End ML projects
  64. Web services vs. streaming for real-time machine learning endpoints
  65. How PyTorch Lightning became the first ML framework to run continuous integration on TPUs
  66. Best practices in data cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data.
  67. The ultimate guide to building maintainable Machine Learning pipelines using DVC
  68. Continuous Machine Learning (CML) is CI/CD for Machine Learning Projects (DVC)
  69. What I learned from looking at 200 machine learning tools
  70. Big Data & AI Landscape
  71. Deploying Machine Learning Models as Data, not Code — A better match? MLOps using omega|ml
  72. “Thou shalt always scale” — 10 commandments of MLOps
  73. Three Risks in Building Machine Learning Systems
  74. Deploying R Models with MLflow and Docker

MLOps Papers

  1. Studer, S., Bui, T.B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S. and Mueller, K.R., 2020. "Towards CRISP-ML (Q): A Machine Learning Process Model with Quality Assurance Methodology". arXiv
  2. Building a Reproducible Machine Learning Pipeline
  3. A Systems Perspective to Reproducibility in Production Machine Learning Domain
  4. Hidden Technical Debt in Machine Learning Systems
  5. Scaling Machine Learning as a Service (Uber)
  6. What’s your ML Test Score? A rubric for ML production systems
  7. Adversarial Machine Learning Reading List
  8. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
  9. Workshop on MLOps Systems. 2020 Third Conference on Machine Learning and Systems (MLSys)
  10. sensAI: Fast ConvNets Serving on Live Data via Class Parallelism. Guanhua Wang, Zhuang Liu, Siyuan Zhuang, Brandon Hsieh, Joseph Gonzalez and Ion Stoica.
  11. Towards Automated ML Model Monitoring: Measure, Improve and Quantify Data Quality. Tammo Rukat, Dustin Lange, Sebastian Schelter and Felix Biessmann.
  12. Towards Automating the AI Operations Lifecycle. Matthew Arnold, Jeff Boston, Michael Desmond, Evelyn Duesterwald, Benjamin Elder, Anupama Murthi, Jiri Navratil and Darrell Reimer.
  13. Efficient Scheduling of DNN Training on Multitenant Clusters. Deepak Narayanan, Keshav Santhanam, Amar Phanishayee and Matei Zaharia.
  14. Towards Complaint-driven ML Workflow Debugging. Weiyuan Wu, Lampros Flokas, Eugene Wu and Jiannan Wang.
  15. PerfGuard: Deploying ML-for-Systems without Performance Regressions. H M Sajjad Hossain, Lucas Rosenblatt, Gilbert Antonius, Irene Shaffer, Remmelt Ammerlaan, Abhishek Roy, Markus Weimer, Hiren Patel, Marc Friedman, Shi Qiao, Peter Orenberg, Soundarajan Srinivasan and Alekh Jindal.
  16. Implicit Provenance for Machine Learning Artifacts. Alexandru A. Ormenisan, Mahmoud Ismail, Seif Haridi and Jim Dowling.
  17. Addressing the Memory Bottleneck in AI Model-Training. David Ojika, Bhavesh Patel, G Anthony Reina, Trent Boyer, Chad Martin and Prashant Shah.
  18. Simulating Performance of ML Systems with Offline Profiling. Hongming Huang, Peng Cheng, Hong Xu and Yongqiang Xiong.
  19. A Viz Recommendation System: ML Lifecycle at Tableau. Kazem Jahanbakhsh, Eric Borchu, Mya Warren, Xiang-Bo Mao and Yogesh Sood.
  20. CodeReef: an open portal for cross-platform MLOps and reproducible benchmarking. Grigori Fursin, Herve Guillou and Nicolas Essayan.
  21. Towards split learning at scale: System design. Iker Rodríguez, Eduardo Muñagorri, Alberto Roman, Abhishek Singh, Praneeth Vepakomma and Ramesh Raskar.
  22. MLBox: Towards Reproducible ML. Victor Bittorf, Xinyuan Huang, Peter Mattson, Debojyoti Dutta, David Aronchick, Emad Barsoum, Sarah Bird, Sergey Serebryakov, Natalia Vassilieva, Tom St. John, Grigori Fursin, Srini Bala, Sivanagaraju Yarramaneni, Alka Roy, David Kanter and Elvira Dzhuraeva.
  23. Conversational Applications and Natural Language Understanding Services at Scale. Minh Tue Vo Thanh and Vijay Ramakrishnan.
  24. Towards Distribution Transparency for Supervised ML With Oblivious Training Functions. Moritz Meister, Sina Sheikholeslami, Robin Andersson, Alexandru Ormenisan and Jim Dowling.
  25. Tools for machine learning experiment management. Vlad Velici and Adam Prügel-Bennett.
  26. MLPM: Machine Learning Package Manager. Xiaozhe Yao.
  27. Common Problems with Creating Machine Learning Pipelines from Existing Code. Katie O’Leary, Makoto Uchida.
  28. Overton: A Data System for Monitoring and Improving Machine-Learned Products, Apple.
  29. Reliance on Metrics is a Fundamental Challenge for AI
  30. "Assuring the machine learning lifecycle: Desiderata, methods, and challenges." Ashmore, Rob, Radu Calinescu, and Colin Paterson. (2019)
  31. "Machine learning testing: Survey, landscapes and horizons." Zhang, Jie M., et al. IEEE Transactions on Software Engineering (2020).
  32. "Teaching Software Engineering for AI-Enabled Systems." Kästner, Christian, and Eunsuk Kang. arXiv (2020).
  33. "Explainable machine learning in deployment." Bhatt, Umang, et al. Proceedings of the Conference on Fairness, Accountability, and Transparency. 2020.
  34. "Studying software engineering patterns for designing machine learning systems." Washizaki, Hironori, Hiromu Uchida, Foutse Khomh, and Yann-Gaël Guéhéneuc. In 2019 10th International Workshop on Empirical Software Engineering in Practice (IWESEP)
  35. Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S.A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T. and Ogilvie, P., 2020, June. Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle. In Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning
  36. Karlaš, B., Interlandi, M., Renggli, C., Wu, W., Zhang, C., Mukunthu, D., Babu, I., Edwards, J., Lauren, C., Xu, A. and Weimer, M., Building Continuous Integration Services for Machine Learning. KDD 2020
  37. Workshop at ICML 2020: "Challenges in Deploying and Monitoring Machine Learning Systems" (Accepted Papers)

Talks About MLOps

  1. DeliveryConf 2020. "Continuous Delivery For Machine Learning: Patterns And Pains" by Emily Gorcenski
  2. MLOps Conference: Talks from 2019
  3. A CI/CD Framework for Production Machine Learning at Massive Scale (using Jenkins X and Seldon Core)
  4. MLOps Virtual Event (Databricks)
  5. MLOps NY conference 2019
  6. MLOps.community YouTube Channel

Existing ML Systems

  1. Introducing FBLearner Flow: Facebook’s AI backbone
  2. TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
  3. Getting started with Kubeflow Pipelines
  4. Meet Michelangelo: Uber’s Machine Learning Platform
  5. Meson: Workflow Orchestration for Netflix Recommendations
  6. What are Azure Machine Learning pipelines?
  7. Uber ATG’s Machine Learning Infrastructure for Self-Driving Vehicles
  8. An overview of ML development platforms
  9. Snorkel AI: Putting Data First in ML Development

Machine Learning

  1. Book, Aurélien Géron,"Hands-On Machine Learning with Scikit-Learn and TensorFlow"
  2. Foundations of Machine Learning
  3. Best Resources to Learn Machine Learning
  4. Curated List of Libraries For a Faster Machine Learning Workflow
  5. Awesome TensorFlow
  6. "Papers with Code" - Browse the State-of-the-Art in Machine Learning
  7. Zhi-Hua Zhou. 2012. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC.
  8. Feature Engineering for Machine Learning. Principles and Techniques for Data Scientists.By Alice Zheng, Amanda Casari
  9. Google Research: Looking Back at 2019, and Forward to 2020 and Beyond
  10. O’Reilly: The road to Software 2.0
  11. Machine Learning and Data Science Applications in Industry
  12. Deep Learning for Anomaly Detection
  13. Federated Learning for Mobile Keyboard Prediction
  14. Federated Learning. Building better products with on-device data and privacy on default
  15. Federated Learning: Collaborative Machine Learning without Centralized Training Data
  16. Book: Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019
  17. Book: Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren. "Automated Machine Learning". Springer,2019.
  18. ML resources by topic, curated by the community.
  19. An Introduction to Machine Learning Interpretability, by Patrick Hall, Navdeep Gill, 2nd Edition. O'Reilly 2019
  20. Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
  21. Paper: "Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence", by Sebastian Raschka, Joshua Patterson, and Corey Nolet. 2020
  22. Distill: Machine Learning Research
  23. AtHomeWithAI: Curated Resource List by DeepMind
  24. Awesome Data Science
  25. Intro to probabilistic programming. A use case using Tensorflow-Probability (TFP)

Software Engineering

  1. The Twelve Factors
  2. Book "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations", 2018 by Nicole Forsgren et.al
  3. Book "The DevOps Handbook" by Gene Kim, et al. 2016
  4. State of DevOps 2019
  5. Clean Code concepts adapted for machine learning and data science.

Product Management for ML/AI

  1. What you need to know about product management for AI. A product manager for AI does everything a traditional PM does, and much more.
  2. The People + AI Guidebook
  3. User Needs + Defining Success
  4. Building machine learning products: a problem well-defined is a problem half-solved.
  5. Talk: Designing Great ML Experiences (Apple)

The Economics of ML/AI

  1. Book: "Prediction Machines: The Simple Economics of Artificial Intelligence"
  2. Book: "The AI Organization" by David Carmona
  3. Book: "Succeeding with AI". 2020. By Veljko Krunic. Manning Publications
  4. A list of articles about AI and the economy
  5. Gartner AI Trends 2019
  6. Global AI Survey: AI proves its worth, but few scale impact
  7. Getting started with AI? Start here! Everything you need to know to dive into your project
  8. 11 questions to ask before starting a successful Machine Learning project
  9. What AI still can’t do
  10. Demystifying AI Part 4: What is an AI Canvas and how do you use it?
  11. A Data Science Workflow Canvas to Kickstart Your Projects
  12. Is your AI project a nonstarter? Here’s a reality check(list) to help you avoid the pain of learning the hard way
  13. What is THE main reason most ML projects fail?
  14. Designing great data products. The Drivetrain Approach: A four-step process for building data products.
  15. The New Business of AI (and How It’s Different From Traditional Software)
  16. The idea maze for AI startups
  17. The Enterprise AI Challenge: Common Misconceptions
  18. Misconception 1 (of 5): Enterprise AI Is Primarily About The Technology
  19. Misconception 2 (of 5): Automated Machine Learning Will Unlock Enterprise AI
  20. Three Principles for Designing ML-Powered Products
  21. A Step-by-Step Guide to Machine Learning Problem Framing
  22. AI adoption in the enterprise 2020
  23. How Adopting MLOps can Help Companies With ML Culture?
  24. Weaving AI into Your Organization
  25. What to Do When AI Fails
  26. Introduction to Machine Learning Problem Framing
  27. Structured Approach for Identifying AI Use Cases
  28. Book: "Machine Learning for Business" by Doug Hudgeon, Richard Nichol, Oreilly
  29. Why Commercial Artificial Intelligence Products Do Not Scale (FemTech)
  30. Google Cloud’s AI Adoption Framework (White Paper)
  31. Data Science Project Management

Model Governance, Ethics, Responsible AI

  1. What are model governance and model operations? A look at the landscape of tools for building and deploying robust, production-ready machine learning models
  2. Specialized tools for machine learning development and model governance are becoming essential. Why companies are turning to specialized machine learning tools like MLflow.
  3. What are model governance and model operations? – O’Reilly
  4. Book: "Practical Fairness". 2020. By Aileen Nielsen. O'Reilly Media, Inc.
  5. AI Fairness 360, A Step Towards Trusted AI - IBM Research
  6. Responsible AI
  7. TensorFlow Privacy
  8. Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow
  9. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
  10. Programming Fairness in Algorithms. Understanding and combating issues of fairness in supervised learning.
  11. Secure, privacy-preserving and federated machine learning in medical imaging

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