- [LightGBM](https://github.com/microsoft/LightGBM) - A fast, distributed, high performance gradient boosting framework
- [Explainable Boosting Machines](https://github.com/interpretml/interpret) - interpretable model developed in Microsoft Research using bagging, gradient boosting, and automatic interaction detection to estimated generalized additive models.
### AutoML
- [Neural Network Intelligence](https://github.com/microsoft/nni) - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
- [Archai](https://github.com/microsoft/archai) - Reproducible Rapid Research for Neural Architecture Search (NAS).
- [FLAML](https://github.com/microsoft/FLAML) - A fast and lightweight AutoML library.
- [Azure Automated Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml) - Automated Machine Learning for Tabular data (regression, classification and forecasting) by Azure Machine Learning
### Neural Network
- [bayesianize](https://github.com/microsoft/bayesianize) - A Bayesian neural network wrapper in pytorch.
- [O-CNN](https://github.com/microsoft/O-CNN) - Octree-based convolutional neural networks for 3D shape analysis.
- [ResNet](https://github.com/KaimingHe/deep-residual-networks) - deep residual network.
- [CNTK](https://github.com/microsoft/CNTK) - microsoft cognitive toolkit (CNTK), open source deep-learning toolkit.
- [Microsoft Vision Model ResNet50](https://www.microsoft.com/en-us/research/blog/microsoft-vision-model-resnet-50-combines-web-scale-data-and-multi-task-learning-to-achieve-state-of-the-art/) - a large pretrained vision ResNet-50 model using search engine's web-scale image data.
- [Oscar](https://github.com/microsoft/Oscar) - Object-Semantics Aligned Pre-training for Vision-Language Tasks.
### Time Series
- [luminol](https://github.com/linkedin/luminol) - anomaly detection and correlation library.
### NLP
- [T-ULRv2](https://www.microsoft.com/en-us/research/blog/microsoft-turing-universal-language-representation-model-t-ulrv2-tops-xtreme-leaderboard/) - Turing multilingual language model.
- [Turing-NLG](https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/) - Turing Natural Language Generation, 17 billion-parameter language model.
- [DeBERTa](https://github.com/microsoft/DeBERTa) - Decoding-enhanced BERT with Disentangled Attention
- [UniLM](https://github.com/microsoft/unilm) - Unified Language Model Pre-training / Pre-training for NLP and Beyond
- [Unicoder](https://github.com/microsoft/Unicoder) - Unicoder model for understanding and generation.
- [NeuronBlocks](https://github.com/microsoft/NeuronBlocks) - building your nlp dnn models like playing lego
- [Multilingual Model Transfer](https://github.com/microsoft/Multilingual-Model-Transfer) - new deep learning models for bootstrapping language understanding models for languages with no labeled data using labeled data from other languages.
- [MT-DNN](https://github.com/microsoft/MT-DNN) - multi-task deep neural networks for natural language understanding.
- [OpenKP](https://github.com/microsoft/OpenKP) - automatically extracting keyphrases that are salient to the document meanings is an essential step in semantic document understanding.
- [DeText](https://github.com/linkedin/detext) - a deep neural text understanding framework for ranking and classification tasks.
### Interactive Machine Learning
- [Vowpal Wabbit](https://vowpalwabbit.org/) - fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more.
### Recommendation
- [Recommenders](https://github.com/microsoft/recommenders) - examples and best practics for building recommendation systems (A2SVD, DKN, xDeepFM, LightGBM, LSTUR, NAML, NPA, NRMS, RLRMC, SAR, Vowpal Wabbit are invented/contributed by Microsoft).
- [GDMIX](https://github.com/linkedin/gdmix) - A deep ranking personalization framework
### Distributed
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
- [MMLSpark](https://github.com/Azure/mmlspark) - machine learning library on spark.
- [pyton-ml](https://github.com/linkedin/photon-ml) - a scalable machine learning library on apache spark.
- [TonY](https://github.com/linkedin/TonY) - framwork to natively run deep learning frameworks on apache hadoop.
### Casual Inference
- [EconML](https://github.com/microsoft/EconML) - Python package for estimating heterogeneous treatment effects from observational data via machine learning.
- [DoWhy](https://github.com/microsoft/dowhy) - Python library for causal inference that supports explicit modeling and testing of causal assumptions.
### Responsible AI
- [InterpretML](https://interpret.ml/) - a toolkit to help understand models and enable responsbile machine learning.
- [Interpret Community](https://github.com/interpretml/interpret-community) - extends interpret repo with additional interpretability techniques and utility functions.
- [DiCE](https://github.com/interpretml/DiCE) - diverse counterfactual explanations.
- [Interpret-Text](https://github.com/interpretml/interpret-text) - state-of-the-art explainers for text-based ml models and visualize with dashboard.
- [fairlearn](https://github.com/fairlearn/fairlearn) - python package to assess and improve fairness of machine learning models.
- [RobustDG](https://github.com/microsoft/robustdg) - Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
- [SHAP](https://github.com/slundberg/shap) - a game theoretic approach to explain the output of any machine learning model (scott lundbert, Microsoft Research).
- [LIME](https://github.com/marcotcr/lime) - explaining the predictions of any machine learning classifier (Marco, Microsoft Research).
- [BackwardCompatibilityML](https://github.com/microsoft/BackwardCompatibilityML) - Project for open sourcing research efforts on Backward Compatibility in Machine Learning
- [confidential-ml-utils](https://github.com/Azure/confidential-ml-utils) - Python utilities for training and deploying ML models against data you can't see.
- [presidio](https://github.com/Microsoft/presidio) - context aware, pluggable and customizable data protection and anonymization service for text and images.
- [Confidential ONNX Inference Server](https://github.com/microsoft/onnx-server-openenclave) - An Open Enclave port of the ONNX inference server with data encryption and attestation capabilities to enable confidential inference on Azure Confidential Computing.
- [Responsible-AI-Widgets](https://github.com/microsoft/responsible-ai-widgets) - responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
- [Error Analysis](https://erroranalysis.ai/) - A toolkit to help analyze and improve model accuracy.
### Optimization
- [ONNXRuntime](https://github.com/microsoft/onnxruntime) - cross-platfom, high performance ML inference and training accelerator.
- [ONNX Runtime Training Examples](https://github.com/microsoft/onnxruntime-training-examples) - examples for using onnx runtime for model training.
- [ONNS Converter](https://github.com/microsoft/onnxconverter-common) - common utilities for onnx converters.
- [ONNX.js](https://github.com/microsoft/onnxjs) - run onnx models using javascript.
- [ONNX.js Demo](https://github.com/microsoft/onnxjs-demo) - demos for ONNX.js.
- [Olive](https://github.com/microsoft/OLive) - a sequence of docker images that automates the process of ONNX model shipping.
- [Hummingbird](https://github.com/microsoft/hummingbird) - compile trained ml model into tensor computation for faster inference.
- [MMdnn](https://github.com/microsoft/MMdnn) - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization.
- [inifinibatch](https://github.com/microsoft/infinibatch) - Efficient, check-pointed data loading for deep learning with massive data sets.
- [InferenceSchema](https://github.com/Azure/InferenceSchema) - Schema decoration for inference code
- [nnfusion](https://github.com/microsoft/nnfusion) - flexible and efficient deep neural network compiler.
### Reinforcement Learning
- [AirSim](https://github.com/microsoft/AirSim) - open source simulator for autonomous vehicles build on unreal engine / unity from microsoft research.
- [TextWorld](https://github.com/microsoft/TextWorld) - TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
- [Moab](https://github.com/microsoft/moab) - Project Moab, a new open-source balancing robot to help engineers and developers learn how to build real-world autonomous control systems with Project Bonsai.
- [Training Data-Driven or Surrogate Simulators](https://github.com/microsoft/datadrivenmodel) - build simulation from data for use in RL and Bonsai platform for machine teaching.
- [Bonsai Python SDK](https://github.com/BonsaiAI/bonsai3-py) - A python library for integrating data sources with Bonsai BRAIN.
### Windows
- [Windows Machine Learning](https://github.com/microsoft/Windows-Machine-Learning) - Machine Learning on Windows.
### Datasets
- [COCO Dataset](https://cocodataset.org/#home) - COCO is a large-scale object detection, segmentation, and captioning dataset.
- [MS MARCO](https://microsoft.github.io/msmarco/) - collection of datasets focused on deep learning in search.
- [InnerEye CreateDataset](https://github.com/microsoft/InnerEye-CreateDataset) - InnerEye dataset creation tool for InnerEye-DeepLearning library. Transforms DICOM data into mask for training Deep Learning models.
- [Sepsis Cohort from MIMIC III](https://github.com/microsoft/mimic_sepsis) - Sepsis cohort from MIMIC dataset.
### Debug
- [tensorwatch](https://github.com/microsoft/tensorwatch) - debugging, monitoring and visualization for python machine learning and data science.
- [PYRIGHT](https://github.com/microsoft/pyright) - static type checker for python.
- [Bench ML](https://github.com/microsoft/benchml) - Python library to benchmark popular pre-built cloud AI APIs.
- [debugpy](https://github.com/microsoft/debugpy) - An implementation of the Debug Adapter Protocol for Python
- [kineto](https://github.com/pytorch/kineto) - A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters contributed by Azure AI Platform team.
### Pipeline
- [GitHub Actions](https://github.com/features/actions) - Automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.
- [Azure Pipelines](https://azure.microsoft.com/en-us/services/devops/pipelines/) - Automate your builds and deployments with Pipelines so you spend less time with the nuts and bolts and more time being creative.
- [Dagli](https://github.com/linkedin/dagli) - framework for defining machine learning models, including feature generation and transformations as DAG.
### Platform
- [AI for Earth API Platform](https://github.com/microsoft/AIforEarth-API-Platform) - distributed infrastructure designed to provide a secure, scalable, and customizable API hosting, designed to handle the needs of long-running/asynchronous machine learning model inference.
- [HivedDScheduler](https://github.com/microsoft/hivedscheduler) - Kubernetes Scheduler for Deep Learning.
- [Open Platfom for AI (OpenPAI](https://github.com/Microsoft/pai) - resource scheduling and cluster management for AI.
- [OpenPAI Runtime](https://github.com/microsoft/openpai-runtime) - Runtime for deep learning workload.
- [MLOS](https://github.com/microsoft/MLOS) - Data Science powered infrastructure and methodology to democratize and automate Performance Engineering.
- [Platform for Situated Intelligence](https://github.com/Microsoft/psi) - an open-source framework for multimodal, integrative AI.
- [Qlib](https://github.com/microsoft/qlib) - an AI-oriented quantitative investment platform.
### Tagging
- [TagAnomaly](https://github.com/microsoft/TagAnomaly) - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
- [Visual Studio Code](https://github.com/microsoft/vscode) - Code editor redefined and optimized for building and debugging modern web and cloud applications.
### Sample Code
- [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth-tech-resources)
- [acoustic-bird-detection](https://github.com/microsoft/acoustic-bird-detection) - Tutorial: Accurate Bioacoustic Species Detection from Small Numbers of Training Clips Using the Biophony Model
- [beluasound](https://github.com/microsoft/belugasounds) - Using machine learning to detect beluga whale calls in hydrophone recordings.
- [arcticseals](https://github.com/microsoft/arcticseals) - detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
- [Camera Trap Tool](https://github.com/microsoft/CameraTraps) - tools for training and running detectors and classifiers for wildlife images collected from motion-triggered cameras.
- [News Threads](https://github.com/microsoft/News-Threads) - The News Threads project analyzes news articles to help find similarities between news articles and trace news provenance across time.
- [InnerEye DeepLearning](https://github.com/microsoft/InnerEye-DeepLearning) - Medical Imaging Deep Learning library to train and deploy models on Azure Machine Learning and Azure Stack
- [Deep Seismic](https://github.com/microsoft/seismic-deeplearning) - Deep Learning for Seismic Imaging and Interpretation
- [Multi-species bioacoustic classification](https://github.com/microsoft/Multi_Species_Bioacoustic_Classification) - Multi-species bioacoustic classification using deep learning algorithms.
- [Nestle Acne Assessment](https://github.com/microsoft/nestle-acne-assessment) - deep learning models to assess the acne severity level based on selfie images.
- [Visual Analogies](https://github.com/microsoft/art) - exploring the connections between artworks with deep "Visual Analogies".
- [Forecasting Best Practices](https://github.com/microsoft/forecasting) - time series forecasting best practices & examples.
- [Computer Vision Recipes](https://github.com/microsoft/computervision-recipes) - best practices, code samples, and documentation for Computer Vision.
### Workshop
:runner: coming soon
### Book
- [PRML](https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/) - Pattern Recognition and Machine Learning by Christopher Bishop
- [Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) - Basic Theory for Data Science.
- [Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning](https://www.amazon.com/dp/1789807557/ref=cm_sw_r_tw_dp_SS0HC0EA2GXPK4QHF087)
- [Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions](https://www.amazon.com/dp/149205559X/ref=cm_sw_r_tw_dp_EN0VKVBDE5MZ1VK39K3F)
## Learning
- [Microsoft Learn](https://docs.microsoft.com/en-us/learn/) - Learning contents for Microsoft technology
- [Data Science for Manager](https://github.com/microsoft/datascience4managers) - Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions
- [Github Learning Lab](https://lab.github.com/) - learning contents for Github technology.
### Blog, News & Webinar
- [channel9 - AI Show](https://channel9.msdn.com/Shows/AI-Show) - videos for developers from people building Microsoft products and services.
- [Microsoft Open Source Blog](https://cloudblogs.microsoft.com/opensource/) - blog about microsoft open source technology.
- [Microsoft Research Event, Conference & Webinars](https://www.microsoft.com/en-us/research/webinar/) - Events, Conferences & Webinars by Microsoft Research.
<br>
---
## Contributing
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## Reporting Security Issues
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Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
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## Preferred Languages
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## Policy
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
Keita Onabuta