MLOps Core |
| Machine Learning Operations: You Design It, You Train It, You Run It! | | | |
| MLOps SIG Specification | 16 | almost 5 years ago | |
| ML in Production | | | |
| Awesome production machine learning: State of MLOps Tools and Frameworks | 17,404 | 4 days ago | |
| Udemy “Deployment of ML Models” | | | |
| Full Stack Deep Learning | | | |
| Engineering best practices for Machine Learning | | | |
| Putting ML in Production | | | |
| Stanford MLSys Seminar Series | | | |
| IBM ML Operationalization Starter Kit | 37 | about 4 years ago | |
| Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products. | | | |
| MLOps (Machine Learning Operations) Fundamentals on GCP | | | |
| ML full Stack preparation | | | |
| MLOps Guide: Theory and Implementation | | | |
| Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning. | | | |
| MLOps maturity assessment | 48 | over 1 year ago | |
MLOps Communities |
| MLOps.community | | | |
| CDF Special Interest Group - MLOps | 598 | 3 months ago | |
| RsqrdAI - Robust and Responsible AI | | | |
| DataTalks.Club | | | |
| Synthetic Data Community | | | |
| MLOps World Community | | | |
| Marvelous MLOps | | | |
MLOps Courses |
| MLOps Zoomcamp (free) | 11,012 | 26 days ago | |
| Coursera's Machine Learning Engineering for Production (MLOps) Specialization | | | |
| Udacity Machine Learning DevOps Engineer | | | |
| Made with ML | | | |
| Udacity LLMOps: Building Real-World Applications With Large Language Models | | | |
MLOps Books |
| “Machine Learning Engineering” by Andriy Burkov, 2020 | | | |
| "ML Ops: Operationalizing Data Science" by David Sweenor, Steven Hillion, Dan Rope, Dev Kannabiran, Thomas Hill, Michael O'Connell | | | |
| "Building Machine Learning Powered Applications" by Emmanuel Ameisen | | | |
| "Building Machine Learning Pipelines" by Hannes Hapke, Catherine Nelson, 2020, O’Reilly | | | |
| "Managing Data Science" by Kirill Dubovikov | | | |
| "Accelerated DevOps with AI, ML & RPA: Non-Programmer's Guide to AIOPS & MLOPS" by Stephen Fleming | | | |
| "Evaluating Machine Learning Models" by Alice Zheng | | | |
| Agile AI. 2020. By Carlo Appugliese, Paco Nathan, William S. Roberts. O'Reilly Media, Inc. | | | |
| "Machine Learning Logistics". 2017. By T. Dunning et al. O'Reilly Media Inc. | | | |
| "Machine Learning Design Patterns" by Valliappa Lakshmanan, Sara Robinson, Michael Munn. O'Reilly 2020 | | | |
| "Serving Machine Learning Models: A Guide to Architecture, Stream Processing Engines, and Frameworks" by Boris Lublinsky, O'Reilly Media, Inc. 2017 | | | |
| "Kubeflow for Machine Learning" by Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, Boris Lublinsky | | | |
| "Clean Machine Learning Code" by Moussa Taifi. Leanpub. 2020 | | | |
| E-Book "Practical MLOps. How to Get Ready for Production Models" | | | |
| "Introducing MLOps" by Mark Treveil, et al. O'Reilly Media, Inc. 2020 | | | |
| "Machine Learning for Data Streams with Practical Examples in MOA", Bifet, Albert and Gavald`a, Ricard and Holmes, Geoff and Pfahringer, Bernhard, MIT Press, 2018 | | | |
| "Machine Learning Product Manual" by Laszlo Sragner, Chris Kelly | | | |
| "Data Science Bootstrap Notes" by Eric J. Ma | | | |
| "Data Teams" by Jesse Anderson, 2020 | | | |
| "Data Science on AWS" by Chris Fregly, Antje Barth, 2021 | | | |
| “Engineering MLOps” by Emmanuel Raj, 2021 | | | |
| Machine Learning Engineering in Action | | | |
| Practical MLOps | | | |
| "Effective Data Science Infrastructure" by Ville Tuulos, 2021 | | | |
| AI and Machine Learning for On-Device Development, 2021, By Laurence Moroney. O'Reilly | | | |
| Designing Machine Learning Systems ,2022 by Chip Huyen , O'Reilly | | | |
| Reliable Machine Learning. 2022. By Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood. O'Reilly | | | |
| MLOps Lifecycle Toolkit. 2023. By Dayne Sorvisto. Apress | | | |
| Implementing MLOps in the Enterprise. 2023. By Yaron Haviv, Noah Gift. O'Reilly | | | |
MLOps Articles |
| Continuous Delivery for Machine Learning (by Thoughtworks) | | | |
| What is MLOps? NVIDIA Blog | | | |
| MLSpec: A project to standardize the intercomponent schemas for a multi-stage ML Pipeline. | 7 | about 5 years ago | |
| The 2021 State of Enterprise Machine Learning | | | | State of Enterprise ML 2020: and |
| Organizing machine learning projects: project management guidelines. | | | |
| Rules for ML Project (Best practices) | | | |
| ML Pipeline Template | | | |
| Data Science Project Structure | | | |
| Reproducible ML | 86 | over 5 years ago | |
| ML project template facilitating both research and production phases. | 5 | about 5 years ago | |
| Machine learning requires a fundamentally different deployment approach. As organizations embrace machine learning, the need for new deployment tools and strategies grows. | | | |
| Introducting Flyte: A Cloud Native Machine Learning and Data Processing Platform | | | |
| Why is DevOps for Machine Learning so Different? | | | |
| Lessons learned turning machine learning models into real products and services – O’Reilly | | | |
| MLOps: Model management, deployment and monitoring with Azure Machine Learning | | | |
| Guide to File Formats for Machine Learning: Columnar, Training, Inferencing, and the Feature Store | | | |
| Architecting a Machine Learning Pipeline How to build scalable Machine Learning systems | | | |
| Why Machine Learning Models Degrade In Production | | | |
| Concept Drift and Model Decay in Machine Learning | | | |
| Machine Learning in Production: Why You Should Care About Data and Concept Drift | | | |
| Bringing ML to Production | | | |
| A Tour of End-to-End Machine Learning Platforms | | | |
| MLOps: Continuous delivery and automation pipelines in machine learning | | | |
| AI meets operations | | | |
| What would machine learning look like if you mixed in DevOps? Wonder no more, we lift the lid on MLOps | | | |
| Forbes: The Emergence Of ML Ops | | | |
| Cognilytica Report "ML Model Management and Operations 2020 (MLOps)" | | | |
| Introducing Cloud AI Platform Pipelines | | | |
| A Guide to Production Level Deep Learning | 4,327 | 11 months ago | |
| The 5 Components Towards Building Production-Ready Machine Learning Systems | | | |
| Deep Learning in Production (references about deploying deep learning-based models in production) | 4,294 | 5 months ago | |
| Machine Learning Experiment Tracking | | | |
| The Team Data Science Process (TDSP) | | | |
| MLOps Solutions (Azure based) | 3 | over 4 years ago | |
| Monitoring ML pipelines | | | |
| Deployment & Explainability of Machine Learning COVID-19 Solutions at Scale with Seldon Core and Alibi | 18 | 10 months ago | |
| Demystifying AI Infrastructure | | | |
| Organizing machine learning projects: project management guidelines. | | | |
| The Checklist for Machine Learning Projects (from Aurélien Géron,"Hands-On Machine Learning with Scikit-Learn and TensorFlow") | 6 | over 4 years ago | |
| Data Project Checklist by Jeremy Howard | | | |
| MLOps: not as Boring as it Sounds | | | |
| 10 Steps to Making Machine Learning Operational. Cloudera White Paper | | | |
| MLOps is Not Enough. The Need for an End-to-End Data Science Lifecycle Process. | | | |
| Data Science Lifecycle Repository Template | 176 | over 4 years ago | |
| Template: code and pipeline definition for a machine learning project demonstrating how to automate an end to end ML/AI workflow. | 18 | over 5 years ago | |
| Nitpicking Machine Learning Technical Debt | | | |
| The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups | | | |
| Software Engineering for AI/ML - An Annotated Bibliography | 301 | 3 months ago | |
| Intelligent System. Machine Learning in Practice | | | |
| CMU 17-445/645: Software Engineering for AI-Enabled Systems (SE4AI) | 376 | over 1 year ago | |
| Machine Learning is Requirements Engineering | | | |
| Machine Learning Reproducibility Checklist | | | |
| Machine Learning Ops. A collection of resources on how to facilitate Machine Learning Ops with GitHub. | | | |
| Task Cheatsheet for Almost Every Machine Learning Project A checklist of tasks for building End-to-End ML projects | | | |
| Web services vs. streaming for real-time machine learning endpoints | | | |
| How PyTorch Lightning became the first ML framework to run continuous integration on TPUs | | | |
| The ultimate guide to building maintainable Machine Learning pipelines using DVC | | | |
| Continuous Machine Learning (CML) is CI/CD for Machine Learning Projects (DVC) | | | |
| What I learned from looking at 200 machine learning tools | | | | Update: |
| Big Data & AI Landscape | | | |
| Deploying Machine Learning Models as Data, not Code — A better match? | | | |
| “Thou shalt always scale” — 10 commandments of MLOps | | | |
| Three Risks in Building Machine Learning Systems | | | |
| Blog about ML in production (by maiot.io) | | | |
| Part 1 | | | Back to the Machine Learning fundamentals: How to write code for Model deployment. , , |
| MLOps: Machine Learning as an Engineering Discipline | | | |
| ML Engineering on Google Cloud Platform (hands-on labs and code samples) | 770 | 29 days ago | |
| Deep Reinforcement Learning in Production. The use of Reinforcement Learning to Personalize User Experience at Zynga | | | |
| What is Data Observability? | | | |
| A Practical Guide to Maintaining Machine Learning in Production | | | |
| Part 1 | | | Continuous Machine Learning. , . Part 3 is coming soon |
| The Agile approach in data science explained by an ML expert | | | |
| Here is what you need to look for in a model server to build ML-powered services | | | |
| The problem with AI developer tools for enterprises (and what IKEA has to do with it) | | | |
| Streaming Machine Learning with Tiered Storage | | | |
| Best practices for performance and cost optimization for machine learning (Google Cloud) | | | |
| Lean Data and Machine Learning Operations | | | |
| A Brief Guide to Running ML Systems in Production Best Practices for Site Reliability Engineers | | | |
| AI engineering practices in the wild - SIG | Getting software right for a healthier digital world | | | |
| SE-ML | The 2020 State of Engineering Practices for Machine Learning | | | |
| Awesome Software Engineering for Machine Learning (GitHub repository) | 1,227 | 6 months ago | |
| Sampling isn’t enough, profile your ML data instead | | | |
| Reproducibility in ML: why it matters and how to achieve it | | | |
| 12 Factors of reproducible Machine Learning in production | | | |
| MLOps: More Than Automation | | | |
| Lean Data Science | | | |
| Engineering Skills for Data Scientists | | | |
| DAGsHub Blog. Read about data science and machine learning workflows, MLOps, and open source data science | | | |
| Data Science Project Flow for Startups | | | |
| Data Science Engineering at Shopify | | | |
| Building state-of-the-art machine learning technology with efficient execution for the crypto economy | | | |
| Completing the Machine Learning Loop | | | |
| Deploying Machine Learning Models: A Checklist | | | |
| Global MLOps and ML tools landscape (by MLReef) | | | |
| Why all Data Science teams need to get serious about MLOps | | | |
| MLOps Values (by Bart Grasza) | | | |
| Machine Learning Systems Design (by Chip Huyen) | | | |
| Designing an ML system (Stanford | CS 329 | Chip Huyen) | | | |
| How COVID-19 Has Infected AI Models (about the data drift or model drift concept) | | | |
| Microkernel Architecture for Machine Learning Library. An Example of Microkernel Architecture with Python Metaclass | | | |
| Machine Learning in production: the Booking.com approach | | | |
| What I Learned From Attending TWIMLcon 2021 (by James Le) | | | |
| Designing ML Orchestration Systems for Startups. A case study in building a lightweight production-grade ML orchestration system | | | |
| Towards MLOps: Technical capabilities of a Machine Learning platform | Prosus AI Tech Blog | | | |
| Get started with MLOps A comprehensive MLOps tutorial with open source tools | | | |
| From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker | | | |
| Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more | 431 | almost 3 years ago | |
| Software Engineering for Machine Learning: Characterizing and Detecting Mismatch in Machine-Learning Systems | | | |
| TWIML Solutions Guide | | | |
| How Well Do You Leverage Machine Learning at Scale? Six Questions to Ask | | | |
| Getting started with MLOps: Selecting the right capabilities for your use case | | | |
| The Latest Work from the SEI: Artificial Intelligence, DevSecOps, and Security Incident Response | | | |
| MLOps: The Ultimate Guide. A handbook on MLOps and how to think about it | | | |
| Enterprise Readiness of Cloud MLOps | | | |
| Should I Train a Model for Each Customer or Use One Model for All of My Customers? | | | |
| MLOps-Basics (GitHub repo) | 5,749 | 13 days ago | by |
| Another tool won’t fix your MLOps problems | | | |
| Best MLOps Tools: What to Look for and How to Evaluate Them (by NimbleBox.ai) | | | |
| MLOps vs. DevOps: A Detailed Comparison (by NimbleBox.ai) | | | |
| A Guide To Setting Up Your MLOps Team (by NimbleBox.ai) | | | |
MLOps: Workflow Management |
| Open-source Workflow Management Tools: A Survey by Ploomber | | | |
| How to Compare ML Experiment Tracking Tools to Fit Your Data Science Workflow (by dagshub) | | | |
| 15 Best Tools for Tracking Machine Learning Experiments | | | |
MLOps: Feature Stores |
| Feature Stores for Machine Learning Medium Blog | | | |
| MLOps with a Feature Store | | | |
| Feature Stores for ML | | | |
| Hopsworks: Data-Intensive AI with a Feature Store | 1,139 | 23 days ago | |
| Feast: An open-source Feature Store for Machine Learning | 5,514 | 5 days ago | |
| What is a Feature Store? | | | |
| ML Feature Stores: A Casual Tour | | | |
| Comprehensive List of Feature Store Architectures for Data Scientists and Big Data Professionals | | | |
| ML Engineer Guide: Feature Store vs Data Warehouse (vendor blog) | | | |
| Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression (DoorDash blog) | | | |
| Feature Stores: Variety of benefits for Enterprise AI. | | | |
| Feature Store as a Foundation for Machine Learning | | | |
| ML Feature Serving Infrastructure at Lyft | | | |
| Feature Stores for Self-Service Machine Learning | | | |
| The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models. | | | |
| Is There a Feature Store Over the Rainbow? How to select the right feature store for your use case | | | |
MLOps: Data Engineering (DataOps) |
| The state of data quality in 2020 – O’Reilly | | | |
| Why We Need DevOps for ML Data | | | |
| Data Preparation for Machine Learning (7-Day Mini-Course) | | | |
| Best practices in data cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data. | | | |
| 17 Strategies for Dealing with Data, Big Data, and Even Bigger Data | | | |
| DataOps Data Architecture | | | |
| Data Orchestration — A Primer | | | |
| 4 Data Trends to Watch in 2020 | | | |
| CSE 291D / 234: Data Systems for Machine Learning | | | |
| A complete picture of the modern data engineering landscape | 12,308 | over 2 years ago | |
| Continuous Integration for your data with GitHub Actions and Great Expectations. One step closer to CI/CD for your data pipelines | | | |
| Emerging Architectures for Modern Data Infrastructure | | | |
| Awesome Data Engineering. Learning path and resources to become a data engineer | | | |
| Part 1 | | | Data Quality at Airbnb | |
| DataHub: Popular metadata architectures explained | | | |
| Financial Times Data Platform: From zero to hero. An in-depth walkthrough of the evolution of our Data Platform | | | |
| Alki, or how we learned to stop worrying and love cold metadata (Dropbox) | | | |
| A Beginner's Guide to Clean Data. Practical advice to spot and avoid data quality problems (by Benjamin Greve) | | | |
| ML Lake: Building Salesforce’s Data Platform for Machine Learning | | | |
| Data Catalog 3.0: Modern Metadata for the Modern Data Stack | | | |
| Metadata Management Systems | | | |
| Essential resources for data engineers (a curated recommended read and watch list for scalable data processing) | | | |
| Comprehensive and Comprehensible Data Catalogs: The What, Who, Where, When, Why, and How of Metadata Management (Paper) | | | |
| What I Learned From Attending DataOps Unleashed 2021 (byJames Le) | | | |
| Uber's Journey Toward Better Data Culture From First Principles | | | |
| Cerberus - lightweight and extensible data validation library for Python | | | |
| Design a data mesh architecture using AWS Lake Formation and AWS Glue. AWS Big Data Blog | | | |
| Data Management Challenges in Production Machine Learning (slides) | | | |
| The Missing Piece of Data Discovery and Observability Platforms: Open Standard for Metadata | | | |
| Automating Data Protection at Scale | | | |
| A curated list of awesome pipeline toolkits | 6,148 | 25 days ago | |
| Data Mesh Archtitecture | | | |
| The Essential Guide to Data Exploration in Machine Learning (by NimbleBox.ai) | | | |
| Finding millions of label errors with Cleanlab | | | |
MLOps: Model Deployment and Serving |
| AI Infrastructure for Everyone: DeterminedAI | | | |
| Deploying R Models with MLflow and Docker | | | |
| What Does it Mean to Deploy a Machine Learning Model? | | | |
| Software Interfaces for Machine Learning Deployment | | | |
| Batch Inference for Machine Learning Deployment | | | |
| AWS Cost Optimization for ML Infrastructure - EC2 spend | | | |
| CI/CD for Machine Learning & AI | | | |
| Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow | | | |
| 101 For Serving ML Models | | | |
| Deploying Machine Learning models to production — Inference service architecture patterns | | | |
| Serverless ML: Deploying Lightweight Models at Scale | | | |
| Part 1 | | | ML Model Rollout To Production. | |
| Deploying Python ML Models with Flask, Docker and Kubernetes | | | |
| Deploying Python ML Models with Bodywork | | | |
| Framework for a successful Continuous Training Strategy. When should the model be retrained? What data should be used? What should be retrained? A data-driven approach | | | |
| Efficient Machine Learning Inference. The benefits of multi-model serving where latency matters | | | |
| Deploying Hugging Face ML Models in the Cloud with Infrastructure as Code | | | |
MLOps: Testing, Monitoring and Maintenance |
| Building dashboards for operational visibility (AWS) | | | |
| Monitoring Machine Learning Models in Production | | | |
| Effective testing for machine learning systems | | | |
| Unit Testing Data: What is it and how do you do it? | | | |
| How to Test Machine Learning Code and Systems | | | ( ) |
| Wu, T., Dong, Y., Dong, Z., Singa, A., Chen, X. and Zhang, Y., 2020. Testing Artificial Intelligence System Towards Safety and Robustness: State of the Art. IAENG International Journal of Computer Science, 47(3). | | | |
| Multi-Armed Bandits and the Stitch Fix Experimentation Platform | | | |
| A/B Testing Machine Learning Models | | | |
| Data validation for machine learning. Polyzotis, N., Zinkevich, M., Roy, S., Breck, E. and Whang, S., 2019. Proceedings of Machine Learning and Systems | | | |
| Testing machine learning based systems: a systematic mapping | | | |
| Explainable Monitoring: Stop flying blind and monitor your AI | | | |
| WhyLogs: Embrace Data Logging Across Your ML Systems | | | |
| Evidently AI. Insights on doing machine learning in production. (Vendor blog.) | | | |
| The definitive guide to comprehensively monitoring your AI | | | |
| Introduction to Unit Testing for Machine Learning | | | |
| Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance | | | |
| Part 1 | | | Test-Driven Development in MLOps |
| Domain-Specific Machine Learning Monitoring | | | |
| Introducing ML Model Performance Management (Blog by fiddler) | | | |
| What is ML Observability? (Arize AI) | | | |
| Beyond Monitoring: The Rise of Observability (Arize AI & Monte Carlo Data) | | | |
| Model Failure Modes (Arize AI) | | | |
| Quick Start to Data Quality Monitoring for ML (Arize AI) | | | |
| Playbook to Monitoring Model Performance in Production (Arize AI) | | | |
| Robust ML by Property Based Domain Coverage Testing (Blog by Efemarai) | | | |
| Monitoring and explainability of models in production | | | |
| Beyond Monitoring: The Rise of Observability | | | |
| ML Model Monitoring – 9 Tips From the Trenches. (by NU bank) | | | |
| Model health assurance at LinkedIn. By LinkedIn Engineering | | | |
| How to Trust Your Deep Learning Code | | | ( ) |
| Estimating Performance of Regression Models Without Ground-Truth | | | (Using ) |
| How Hyperparameter Tuning in Machine Learning Works (by NimbleBox.ai) | | | |
| |
| MLOps Infrastructure Stack Canvas | | | |
| Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps | | | |
| AI Infrastructure Alliance. Building the canonical stack for AI/ML | | | |
| Linux Foundation AI Foundation | | | |
| Part 1 — Production ML — The Final Stage of the Model Workflow | | | ML Infrastructure Tools for Production | | |
| The MLOps Stack Template (by valohai) | | | |
| Navigating the MLOps tooling landscape | | | |
| MLOps.toys curated list of MLOps projects (by Aporia) | | | |
| Comparing Cloud MLOps platforms, From a former AWS SageMaker PM | | | |
| Machine Learning Ecosystem 101 (whitepaper by Arize AI) | | | |
| Selecting your optimal MLOps stack: advantages and challenges. By Intellerts | | | |
| Infrastructure Design for Real-time Machine Learning Inference. The Databricks Blog | | | |
| The 2021 State of AI Infrastructure Survey | | | |
| AI infrastructure Maturity matrix | | | |
| A Curated Collection of the Best Open-source MLOps Tools. By Censius | | | |
| Best MLOps Tools to Manage the ML Lifecycle (by NimbleBox.ai) | | | |
| The minimum set of must-haves for MLOps | | | |
Talks About MLOps |
| "MLOps: Automated Machine Learning" by Emmanuel Raj | | | |
| DeliveryConf 2020. "Continuous Delivery For Machine Learning: Patterns And Pains" by Emily Gorcenski | | | |
| MLOps Conference: Talks from 2019 | | | |
| Kubecon 2019: Flyte: Cloud Native Machine Learning and Data Processing Platform | | | |
| Kubecon 2019: Running LargeScale Stateful workloads on Kubernetes at Lyft | | | |
| A CI/CD Framework for Production Machine Learning at Massive Scale (using Jenkins X and Seldon Core) | | | |
| MLOps Virtual Event (Databricks) | | | |
| MLOps NY conference 2019 | | | |
| MLOps.community YouTube Channel | | | |
| MLinProduction YouTube Channel | | | |
| Introducing MLflow for End-to-End Machine Learning on Databricks. Spark+AI Summit 2020. Sean Owen | | | |
| MLOps Tutorial #1: Intro to Continuous Integration for ML | | | |
| Machine Learning At Speed: Operationalizing ML For Real-Time Data Streams (2019) | | | |
| Damian Brady - The emerging field of MLops | | | |
| MLOps - Entwurf, Entwicklung, Betrieb (INNOQ Podcast in German) | | | |
| Instrumentation, Observability & Monitoring of Machine Learning Models | | | |
| Efficient ML engineering: Tools and best practices | | | |
| Beyond the jupyter notebook: how to build data science products | | | |
| An introduction to MLOps on Google Cloud | | | (First 19 min are vendor-, language-, and framework-agnostic. @visenger) |
| How ML Breaks: A Decade of Outages for One Large ML Pipeline | | | |
| Clean Machine Learning Code: Practical Software Engineering | | | |
| Machine Learning Engineering: 10 Fundamentale Praktiken | | | |
| Architecture of machine learning systems (3-part series) | | | |
| Machine Learning Design Patterns | | | |
| The laylist that covers techniques and approaches for model deployment on to production | | | |
| ML Observability: A Critical Piece in Ensuring Responsible AI (Arize AI at Re-Work) | | | |
| ML Engineering vs. Data Science (Arize AI Un/Summit) | | | |
| SRE for ML: The First 10 Years and the Next 10 | | | |
| Demystifying Machine Learning in Production: Reasoning about a Large-Scale ML Platform | | | |
| Apply Conf 2022 | | | |
| Databricks' Data + AI Summit 2022 | | | |
| RE•WORK MLOps Summit 2022 | | | |
| Annual MLOps World Conference | | | |
Existing ML Systems |
| Introducing FBLearner Flow: Facebook’s AI backbone | | | |
| TFX: A TensorFlow-Based Production-Scale Machine Learning Platform | | | |
| Accelerate your ML and Data workflows to production: Flyte | | | |
| Getting started with Kubeflow Pipelines | | | |
| Meet Michelangelo: Uber’s Machine Learning Platform | | | |
| Meson: Workflow Orchestration for Netflix Recommendations | | | |
| What are Azure Machine Learning pipelines? | | | |
| Uber ATG’s Machine Learning Infrastructure for Self-Driving Vehicles | | | |
| An overview of ML development platforms | | | |
| Snorkel AI: Putting Data First in ML Development | | | |
| A Tour of End-to-End Machine Learning Platforms | | | |
| Introducing WhyLabs, a Leap Forward in AI Reliability | | | |
| Project: Ease.ml (ETH Zürich) | | | |
| Bodywork: model-training and deployment automation | | | |
| Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more | | | |
| Papers & tech blogs by companies sharing their work on data science & machine learning in production. By Eugen Yan | 27,189 | 3 months ago | |
| How do different tech companies approach building internal ML platforms? (tweet) | | | |
| Declarative Machine Learning Systems | | | |
| StreamING Machine Learning Models: How ING Adds Fraud Detection Models at Runtime with Apache Flink | | | |
Machine Learning |
| Foundations of Machine Learning | | | |
| Best Resources to Learn Machine Learning | | | |
| Awesome TensorFlow | 17,188 | 6 months ago | |
| "Papers with Code" - Browse the State-of-the-Art in Machine Learning | | | |
| Zhi-Hua Zhou. 2012. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC. | | | |
| Feature Engineering for Machine Learning. Principles and Techniques for Data Scientists. By Alice Zheng, Amanda Casari | | | |
| Google Research: Looking Back at 2019, and Forward to 2020 and Beyond | | | |
| O’Reilly: The road to Software 2.0 | | | |
| Machine Learning and Data Science Applications in Industry | 7,228 | 4 months ago | |
| Deep Learning for Anomaly Detection | | | |
| Federated Learning for Mobile Keyboard Prediction | | | |
| Federated Learning. Building better products with on-device data and privacy on default | | | |
| Federated Learning: Collaborative Machine Learning without Centralized Training Data | | | |
| Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T. and Yu, H., 2019. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13(3). Chapters 1 and 2. | | | |
| Federated Learning by FastForward | | | |
| THE FEDERATED & DISTRIBUTED MACHINE LEARNING CONFERENCE | | | |
| Federated Learning: Challenges, Methods, and Future Directions | | | |
| Book: Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019 | | | |
| Book: Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren. "Automated Machine Learning". Springer,2019. | | | |
| ML resources by topic, curated by the community. | | | |
| An Introduction to Machine Learning Interpretability, by Patrick Hall, Navdeep Gill, 2nd Edition. O'Reilly 2019 | | | |
| Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. | 673 | 4 months ago | |
| 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 | | | |
| Distill: Machine Learning Research | | | |
| AtHomeWithAI: Curated Resource List by DeepMind | | | |
| Awesome Data Science | 24,830 | 11 days ago | |
| Intro to probabilistic programming. A use case using Tensorflow-Probability (TFP) | | | |
| Dive into Snorkel: Weak-Superversion on German Texts. inovex Blog | | | |
| Dive into Deep Learning. An interactive deep learning book with code, math, and discussions. Provides NumPy/MXNet, PyTorch, and TensorFlow implementations | | | |
| Data Science Collected Resources (GitHub repository) | 2,874 | about 2 months ago | |
| Set of illustrated Machine Learning cheatsheets | | | |
| "Machine Learning Bookcamp" by Alexey Grigorev | | | |
| 130 Machine Learning Projects Solved and Explained | | | |
| Machine learning cheat sheet | 7,190 | 2 months ago | |
| Stateoftheart AI. An open-data and free platform built by the research community to facilitate the collaborative development of AI | | | |
| Online Machine Learning Courses: 2020 Edition | | | |
| End-to-End Machine Learning Library | | | |
| Machine Learning Toolbox (by Amit Chaudhary) | | | |
| Causality for Machine Learning | | | |
| Causal Inference for the Brave and True | | | |
| Causal Inference | | | |
| A resource list for causality in statistics, data science and physics | 253 | 3 months ago | |
| Learning from data. Caltech | | | |
| Machine Learning Glossary | | | |
| Book: "Distributed Machine Learning Patterns". 2022. By Yuan Tang. Manning | | | |
| Machine Learning for Beginners - A Curriculum | 69,239 | 14 days ago | |
| Making Friends with Machine Learning. By Cassie Kozyrkov | | | |
| Machine Learning Workflow - A Complete Guide (by NimbleBox.ai) | | | |
| Performance Metrics to Monitor in Machine Learning Projects (by NimbleBox.ai) | | | |
Software Engineering |
| The Twelve Factors | | | |
| Book "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations", 2018 by Nicole Forsgren et.al | | | |
| Book "The DevOps Handbook" by Gene Kim, et al. 2016 | | | |
| State of DevOps 2019 | | | |
| Clean Code concepts adapted for machine learning and data science. | 711 | almost 3 years ago | |
| School of SRE | | | |
| 10 Laws of Software Engineering That People Ignore | | | |
| The Patterns of Scalable, Reliable, and Performant Large-Scale Systems | | | |
| The Book of Secret Knowledge | 145,050 | about 2 months ago | |
| SHADES OF CONWAY'S LAW | | | |
| Engineering Practices for Data Scientists | | | |
Product Management for ML/AI |
| What you need to know about product management for AI. A product manager for AI does everything a traditional PM does, and much more. | | | |
| Bringing an AI Product to Market. Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market? | | | |
| The People + AI Guidebook | | | |
| User Needs + Defining Success | | | |
| Building machine learning products: a problem well-defined is a problem half-solved. | | | |
| Talk: Designing Great ML Experiences (Apple) | | | |
| Machine Learning for Product Managers | | | |
| Understanding the Data Landscape and Strategic Play Through Wardley Mapping | | | |
| Techniques for prototyping machine learning systems across products and features | | | |
| Machine Learning and User Experience: A Few Resources | | | |
| AI ideation canvas | | | |
| Ideation in AI | | | |
| 5 Steps for Building Machine Learning Models for Business. By shopify engineering | | | |
| Metric Design for Data Scientists and Business Leaders | | | |
The Economics of ML/AI |
| Book: "Prediction Machines: The Simple Economics of Artificial Intelligence" | | | |
| Book: "The AI Organization" by David Carmona | | | |
| Book: "Succeeding with AI". 2020. By Veljko Krunic. Manning Publications | | | |
| A list of articles about AI and the economy | | | |
| Gartner AI Trends 2019 | | | |
| Global AI Survey: AI proves its worth, but few scale impact | | | |
| Getting started with AI? Start here! Everything you need to know to dive into your project | | | |
| 11 questions to ask before starting a successful Machine Learning project | | | |
| What AI still can’t do | | | |
| Demystifying AI Part 4: What is an AI Canvas and how do you use it? | | | |
| A Data Science Workflow Canvas to Kickstart Your Projects | | | |
| Is your AI project a nonstarter? Here’s a reality check(list) to help you avoid the pain of learning the hard way | | | |
| What is THE main reason most ML projects fail? | | | |
| Designing great data products. The Drivetrain Approach: A four-step process for building data products. | | | |
| The New Business of AI (and How It’s Different From Traditional Software) | | | |
| The idea maze for AI startups | | | |
| The Enterprise AI Challenge: Common Misconceptions | | | |
| Misconception 1 (of 5): Enterprise AI Is Primarily About The Technology | | | |
| Misconception 2 (of 5): Automated Machine Learning Will Unlock Enterprise AI | | | |
| Three Principles for Designing ML-Powered Products | | | |
| A Step-by-Step Guide to Machine Learning Problem Framing | | | |
| AI adoption in the enterprise 2020 | | | |
| How Adopting MLOps can Help Companies With ML Culture? | | | |
| Weaving AI into Your Organization | | | |
| What to Do When AI Fails | | | |
| Introduction to Machine Learning Problem Framing | | | |
| Structured Approach for Identifying AI Use Cases | | | |
| Book: "Machine Learning for Business" by Doug Hudgeon, Richard Nichol, O'reilly | | | |
| Why Commercial Artificial Intelligence Products Do Not Scale (FemTech) | | | |
| Google Cloud’s AI Adoption Framework (White Paper) | | | |
| Data Science Project Management | | | |
| Book: "Competing in the Age of AI" by Marco Iansiti, Karim R. Lakhani. Harvard Business Review Press. 2020 | | | |
| The Three Questions about AI that Startups Need to Ask. The first is: Are you sure you need AI? | | | |
| Taming the Tail: Adventures in Improving AI Economics | | | |
| Managing the Risks of Adopting AI Engineering | | | |
| Get rid of AI Saviorism | | | |
| Collection of articles listing reasons why data science projects fail | 457 | over 3 years ago | |
| How to Choose Your First AI Project by Andrew Ng | | | |
| How to Set AI Goals | | | |
| Expanding AI's Impact With Organizational Learning | | | |
| Potemkin Data Science | | | |
| When Should You Not Invest in AI? | | | |
| Why 90% of machine learning models never hit the market. Most companies lack leadership support, effective communication between teams, and accessible data | | | |
MLOps: People & Processes |
| Scaling An ML Team (0–10 People) | | | |
| The Knowledge Repo project is focused on facilitating the sharing of knowledge between data scientists and other technical roles. | 5,471 | about 1 month ago | |
| Scaling Knowledge at Airbnb | | | |
| Models for integrating data science teams within companies A comparative analysis | | | |
| How to Write Better with The Why, What, How Framework. How to write design documents for data science/machine learning projects? (by Eugene Yan) | | | |
| Technical Writing Courses | | | |
| Building a data team at a mid-stage startup: a short story. By Erik Bernhardsson | | | |
| The Cultural Benefits of Artificial Intelligence in the Enterprise. by Sam Ransbotham, François Candelon, David Kiron, Burt LaFountain, and Shervin Khodabandeh | | | |
Newsletters About MLOps, Machine Learning, Data Science and Co. |
| ML in Production newsletter | | | |
| MLOps.community | | | |
| Andriy Burkov newsletter | | | |
| Decision Intelligence by Cassie Kozyrkov | | | |
| Laszlo's Newsletter about Data Science | | | |
| Data Elixir newsletter for a weekly dose of the top data science picks from around the web. Covering machine learning, data visualization, analytics, and strategy. | | | |
| The Data Science Roundup by Tristan Handy | | | |
| Vicki Boykis Newsletter about Data Science | | | |
| KDnuggets News | | | |
| Analytics Vidhya, Any questions on business analytics, data science, big data, data visualizations tools and techniques | | | |
| Data Science Weekly Newsletter: A free weekly newsletter featuring curated news, articles and jobs related to Data Science | | | |
| The Machine Learning Engineer Newsletter | | | |
| Gradient Flow helps you stay ahead of the latest technology trends and tools with in-depth coverage, analysis and insights. See the latest on data, technology and business, with a focus on machine learning and AI | | | |
| Your guide to AI by Nathan Benaich. Monthly analysis of AI technology, geopolitics, research, and startups. | | | |
| O'Reilly Data & AI Newsletter | | | |
| deeplearning.ai’s newsletter by Andrew Ng | | | |
| Deep Learning Weekly | | | |
| Import AI is a weekly newsletter about artificial intelligence, read by more than ten thousand experts. By Jack Clark. | | | |
| AI Ethics Weekly | | | |
| Announcing Projects To Know, a weekly machine intelligence and data science newsletter | | | |
| TWIML: This Week in Machine Learning and AI newsletter | | | |
| featurestore.org: Monthly Newsletter on Feature Stores for ML | | | |
| DataTalks.Club Community: Slack, Newsletter, Podcast, Weeekly Events | | | |
| Machine Learning Ops Roundup | | | |
| Data Science Programming Newsletter by Eric Ma | | | |
| Marginally Interesting by Mikio L. Braun | | | |
| Synced | | | |
| The Ground Truth: Newsletter for Computer Vision Practitioners | | | |
| SwirlAI: Data Engineering, MLOps and overall Data focused Newsletter by Aurimas Griciūnas | | | |
| Marvelous MLOps | | | |
| Made with ML | | | |
| MLOps Insights Newsletter - 8 episodes covering topics like Model Feedback Vacuums, Deployment Reproducibility and Serverless in the context of MLOps | | | |
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