README

Awesome MLOps   

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

An awesome list of references for MLOps - Machine Learning Operations :point_right: ml-ops.org




Linkedin Dr. Larysa Visengeriyeva




 Table of Contents
|                          |                          |
| -------------------------------- | -------------------------------- |
| MLOps Core | MLOps Communities |
| MLOps Books | MLOps Articles |
| MLOps Workflow Management| MLOps: Feature Stores | 
|MLOps: Data Engineering (DataOps) | MLOps: Model Deployment and Serving |
| MLOps: Testing, Monitoring and Maintenance| MLOps: Infrastructure| 
|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: People & Processes|Newsletters About MLOps, Machine Learning, Data Science and Co.| 



 MLOps Core

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1. Machine Learning Operations: You Design It, You Train It, You Run It!
1. MLOps SIG Specification
1. ML in Production
1. Awesome production machine learning: State of MLOps Tools and Frameworks
1. Udemy “Deployment of ML Models”
1. Full Stack Deep Learning
1. Engineering best practices for Machine Learning
1. :rocket: Putting ML in Production
1. Stanford MLSys Seminar Series
1. IBM ML Operationalization Starter Kit
1. Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products.
1. MLOps (Machine Learning Operations) Fundamentals on GCP
1. ML full Stack preparation
1. MLOps Guide: Theory and Implementation
1. Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning.
1. MLOps maturity assessment




 MLOps Communities

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1. MLOps.community
1. CDF Special Interest Group - MLOps
1. RsqrdAI - Robust and Responsible AI
1. DataTalks.Club
1. Synthetic Data Community
1. MLOps World Community
1. Marvelous MLOps



 MLOps Courses

1. MLOps Zoomcamp (free)
1. Coursera's Machine Learning Engineering for Production (MLOps) Specialization
1. Udacity Machine Learning DevOps Engineer
1. Made with ML
1. Udacity LLMOps: Building Real-World Applications With Large Language Models



 MLOps Books


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