This repository serves as a comprehensive, curated collection of Microsoft's machine learning and deep learning technologies, tools, libraries, and sample code. It targets data scientists, AI engineers, and researchers seeking to leverage Microsoft's advancements across various ML domains, offering a centralized resource for exploration and adoption.

How It Works

The collection is organized by ML domain, including Boosting, AutoML, Neural Networks, Graph & Network, Vision, Time Series, NLP, Online ML, Recommendation, Distributed ML, Causal Inference, Responsible AI, Optimization, and Reinforcement Learning. Each domain lists specific Microsoft-developed or contributed projects, such as LightGBM, FLAML, ONNX Runtime, and EconML, providing links to their respective repositories and documentation. This structured approach allows users to quickly identify and access relevant Microsoft ML technologies.

Quick Start & Requirements

Installation and usage vary significantly by sub-project. Many projects are Python-based and can be installed via pip or Docker. Specific requirements like GPU, CUDA versions, or large datasets are detailed within individual project documentation. Links to official quick-start guides, demos, and full documentation are provided for each listed technology.

Highlighted Details

• Extensive coverage of Microsoft's ML ecosystem, from foundational libraries to specialized tools.
• Includes cutting-edge research projects and production-ready frameworks.
• Features resources for Responsible AI, including fairness, interpretability, and privacy.
• Provides a wide array of sample code, datasets, and learning materials.

Maintenance & Community

This is a Microsoft-maintained collection, with many projects actively developed by Microsoft Research and various product teams. Community engagement is encouraged via contributions, with a Contributor License Agreement (CLA) and Microsoft's Open Source Code of Conduct in place.

Licensing & Compatibility

Licenses vary by sub-project, with many projects released under permissive licenses like MIT or Apache 2.0, facilitating commercial use. However, users must verify the specific license for each component.

Limitations & Caveats

The collection is a directory, not a single installable package. Users must navigate to individual project repositories for installation, dependencies, and specific usage instructions. The breadth of technologies means some projects may be experimental or have varying levels of maturity.