Overview
This repository compiles over 300 case studies from more than 80 companies, documenting production ML use cases such as recommendation, search, anomaly detection, online learning, evaluation, and deployment. The collection aggregates links and short summaries to help engineers learn practical system design decisions from real-world examples.
Key Features
- Broad coverage across industries and ML applications, sourced from official engineering blogs and papers.
- Structured tables and summaries for quick lookup by company, year, or scenario.
- Community-friendly format that is easy to extend and integrate into knowledge systems.
Use Cases
- Learning and postmortem: study production design choices and trade-offs.
- Architecture comparison: reference real-world implementations when evaluating design options.
- Team training: use as curated material for workshops and onboarding.
Technical Highlights
- Markdown-first content with tabular summaries and external references.
- Focus on engineering trade-offs and architecture rather than large code snippets.
- Suitable for integration with research and literature tools to build internal knowledge bases.