A curated directory that organizes real-world machine learning papers and engineering blogs into 31 specific categories. It serves data teams and researchers investigating how organizations implement ML in production.
Project overview
A curated directory that organizes real-world machine learning papers and engineering blogs into 31 specific categories. It serves data teams and researchers investigating how organizations implement ML in production.
Project type
Model Runtime · Data Processing · Infrastructure
Use cases
Search & Research
Deployment
Refer to project documentation
License
MIT
Best for
Data teams, AI engineers, and researchers seeking published organizational case studies on deploying machine learning.
Key capabilities
Provides a table of contents that organizes real-world machine learning papers and engineering blogs into 31 specific categories, including data quality, search, natural language processing, and MLOps platforms.
Limitations and risks
Provides links to external content rather than hosting the full text, models, or datasets within the repository itself.
Getting started
Open the repository on GitHub, browse the Table of Contents, and select a category to view the related articles and engineering publications.
Evidence and sources
README: Curated papers, articles, and blogs on **data science & machine learning in production**.
README: Figuring out how to implement your ML project? Learn how other organizations did it:
README: - **What** real-world results were achieved (so you can better assess ROI ⏰💰📈)