This repository curates papers, articles, and tech blogs detailing the practical implementation of data science and machine learning in production environments. It serves as a valuable resource for ML engineers and practitioners seeking to understand how leading companies frame problems, select techniques, achieve results, and manage the entire ML lifecycle. The collection offers insights into the "why" and "how" of successful ML deployments, enabling better ROI assessment and practical application.

How It Works

The repository is organized by ML lifecycle stages and specific problem domains, such as Data Quality, Feature Stores, Recommendation Systems, and MLOps. Each entry links to a relevant publication or blog post from major tech companies, providing concrete examples of real-world ML challenges and solutions. This curated approach allows users to quickly find case studies relevant to their specific needs, learning from the experiences of industry leaders.

Quick Start & Requirements

This is a curated list of resources, not a software package. No installation or specific requirements are needed beyond a web browser to access the linked content.

Highlighted Details

• Comprehensive coverage across the ML lifecycle, from data engineering to model management and ethics.
• Extensive collection of real-world case studies from prominent tech companies like Google, Netflix, Airbnb, Uber, and Meta.
• Detailed insights into problem framing, technique selection, and achieved results, aiding in ROI assessment.
• Includes links to papers, code repositories, and demos where available.

Maintenance & Community

The repository is maintained by Eugene Yan. Further community engagement or roadmap details are not explicitly mentioned in the README.

Licensing & Compatibility

The repository itself is not a software package and does not have a specific license. The linked content is subject to the licenses and terms of the original publishers.

Limitations & Caveats

This is a static collection of links and does not provide any executable code or interactive tools. The depth of information for each topic varies based on the availability and nature of the linked resources.