Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

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• LightGBM - A fast, distributed, high performance gradient boosting framework.
• LightGBM benchmarking suite - Benchmark tools for LightGBM.
• Explainable Boosting Machines - interpretable model developed in Microsoft Research using bagging, gradient boosting, and automatic interaction detection to estimated generalized additive models.
• Cyclic Boosting Machines - An explainable supervised machine learning algorithm specifically for predicting count-data, such as sales and demand.

• Neural Network Intelligence - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
• Archai - Reproducible Rapid Research for Neural Architecture Search (NAS).
• FLAML - A fast and lightweight AutoML library.
• Azure Automated Machine Learning - Automated Machine Learning for Tabular data (regression, classification and forecasting) by Azure Machine Learning
• Cream - A collection of Microsoft NAS and Vision Transformer work.

• PyMarlin - Lightweight Deep Learning Model Training library based on PyTorch.
• bayesianize - A Bayesian neural network wrapper in pytorch.
• O-CNN - Octree-based convolutional neural networks for 3D shape analysis.
• ResNet - deep residual network.
• CNTK - microsoft cognitive toolkit (CNTK), open source deep-learning toolkit.
• InfiniBatch - Efficient, check-pointed data loading for deep learning with massive data sets.
• Models under Hugging Face - Microsoft shares transformer models at Hugging Face. 51 pretrained models (as of June 28, 2021).
• Muzic - Music Understanding and Generation with Artificial Intelligence.

• graspologic - utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.
• TF Graph Neural Network Samples - tensorFlow implementations of graph neural networks.
• ptgnn - PyTorch Graph Neural Network Library
• StemGNN - spectral temporal graph neural network (StemGNN) for multivariate time-series forecasting.
• SPTAG - a distributed approximate nearest neighborhood search (ANN) library.
• DiskANN - Scalable graph based indices for approximate nearest neighbor search.

• Microsoft Vision Model ResNet50 - a large pretrained vision ResNet-50 model using search engine's web-scale image data.
• Oscar - Object-Semantics Aligned Pre-training for Vision-Language Tasks.
• TorchGeo - a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.
• Swin Transformer - an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

• luminol - anomaly detection and correlation library.
• SR-CNN - Spectral Residual based anomaly detection algorithm, SR-CNN implementation.
• Greykite - flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
• Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery - An implementation of the temporal cluster matching method for detecting change in structure footprints from time series of remotely sensed imagery.
• Microsoft Finance Time Series Forecasting Framework - a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.
• FOST - an easy-use tool for spatial-temporal forecasting

• T-ULRv2 - Turing multilingual language model.
• Turing-NLG - Turing Natural Language Generation, 17 billion-parameter language model.
• DeBERTa - Decoding-enhanced BERT with Disentangled Attention
• UniLM - Unified Language Model Pre-training / Pre-training for NLP and Beyond
• Unicoder - Unicoder model for understanding and generation.
• NeuronBlocks - building your nlp dnn models like playing lego
• 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 - multi-task deep neural networks for natural language understanding.
• inmt - interactive neural machine trainslation-lite
• OpenKP - automatically extracting keyphrases that are salient to the document meanings is an essential step in semantic document understanding.
• DeText - a deep neural text understanding framework for ranking and classification tasks.
• Genalog - an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
• FastFormers - highly efficient transformer models for NLU.
• VERSEAGILITY - a Python-based toolkit to ramp up your custom natural language processing (NLP) task, allowing you to bring your own data and bring models into production. It is a central component of the Microsoft Data Science Toolkit.
• DPU Utilities - Utilities used by the Deep Program Understanding team.
• KEAR - Official code for achieving human parity on CommonsenseQA with External Attention.
• Prompt Engine - A utility library for creating and maintaining prompts for Large Language Models.

• Vowpal Wabbit - fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more.

• 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 - A deep ranking personalization framework
• rankerEval - A fast numpy-based implementation of ranking metrics for information retrieval and recommendation.

• DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
• MMLSpark - machine learning library on spark.
• photon-ml - a scalable machine learning library on apache spark.
• TonY - framework to natively run deep learning frameworks on apache hadoop.
• isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

• EconML - Python package for estimating heterogeneous treatment effects from observational data via machine learning.
• DoWhy - Python library for causal inference that supports explicit modeling and testing of causal assumptions.

• InterpretML - a toolkit to help understand models and enable responsbile machine learning.
  • Interpret Community - extends interpret repo with additional interpretability techniques and utility functions.
  • DiCE - diverse counterfactual explanations.
  • Interpret-Text - state-of-the-art explainers for text-based ml models and visualize with dashboard.
• fairlearn - python package to assess and improve fairness of machine learning models.
• LiFT - linkedin fairness toolkit.
• RobustDG - Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
• SHAP - a game theoretic approach to explain the output of any machine learning model (scott lundbert, Microsoft Research).
• LIME - explaining the predictions of any machine learning classifier (Marco, Microsoft Research).
• BackwardCompatibilityML - Project for open sourcing research efforts on Backward Compatibility in Machine Learning
• confidential-ml-utils - Python utilities for training and deploying ML models against data you can't see.
• presidio - context aware, pluggable and customizable data protection and anonymization service for text and images.
  • Presidio-research - This package features data-science related tasks for developing new recognizers for Presidio.
• Confidential ONNX Inference Server - 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 - responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
• Error Analysis - A toolkit to help analyze and improve model accuracy.
• Secure Data Sandbox - A toolkit for conducting machine learning trials against confidential data.
• shrike - Python utilities to aid "compliant experiment" in Azure Machine Learning - training ML models without seeing the training data.
• HAX Toolkit - The Human-AI eXperience (HAX) Toolkit is a set of practical tools for creating human-AI experiences with people in mind from the beginning.
• GAM Changer - Edit machine learning models to reflect human knowledge and values.
• AdaTest - Find and fix bugs in natural language machine learning models using adaptive testing.

• ONNXRuntime - cross-platfom, high performance ML inference and training accelerator.
  • ONNX Runtime for PyTorch - Accelerate PyTorch models with ONNX Runtime.
  • ONNX Runtime Training Examples - examples for using onnx runtime for model training.
  • ONNX Runtime Inference Examples - examples for using onnx runtime for model inference.
  • ONNX Converter - common utilities for onnx converters.
  • ONNX.js - run onnx models using javascript.
  • ONNX.js Demo - demos for ONNX.js.
  • Olive - a sequence of docker images that automates the process of ONNX model shipping.
• Hummingbird - compile trained ml model into tensor computation for faster inference.
• EdgeML - provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
• DirectML - high-performance, hardware-accelerated DirectX 12 library for machine learning.
• MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization.
• inifinibatch - Efficient, check-pointed data loading for deep learning with massive data sets.
• InferenceSchema - Schema decoration for inference code
• nnfusion - flexible and efficient deep neural network compiler.
• Accera - Open source cross-platform compiler for compute-intensive loops used in AI algorithms, from Microsoft Research.

• AirSim - open source simulator for autonomous vehicles build on unreal engine / unity from microsoft research.

• TextWorld - TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

• 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.

• MARO - multi-agent resource optimization (MARO) platfom.

• Training Data-Driven or Surrogate Simulators - build simulation from data for use in RL and Bonsai platform for machine teaching.

• Bonsai - low code industrial machine teaching platform.

  • Bonsai Python SDK - A python library for integrating data sources with Bonsai BRAIN.

• SEGAR - Sandbox environment for generalizable agent research.

• counterfit - a CLI that provides a generic automation layer for assessing the security of ML models.
• Federated Learning Simulation Framework - a flexible framework for running experiments with PyTorch models in a simulated Federated Learning (FL) environment.
• FLUTE - a platform for conducting high-performance federated learning simulations.

• Windows Machine Learning - Machine Learning on Windows.

• COCO Dataset - COCO is a large-scale object detection, segmentation, and captioning dataset.
• MS MARCO - collection of datasets focused on deep learning in search.
• 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 - Sepsis cohort from MIMIC dataset.
• MIND : Microsoft News Dataset - a large-scale dataset for news recommendation research.
• Dataset for AI for Earth - AIForEarthDataSets is a collection of datasets for AI research.
• ORBIT - a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone.
• EcoFlows - Community-representation to collaborate on labelled AI data for ecological and agricultural scenarios in APAC, updated monthly.

• tensorwatch - debugging, monitoring and visualization for python machine learning and data science.
• PYRIGHT - static type checker for python.
• Bench ML - Python library to benchmark popular pre-built cloud AI APIs.
• debugpy - An implementation of the Debug Adapter Protocol for Python
• kineto - A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters contributed by Azure AI Platform team.
• SuperBenchmark - a benchmarking and diagnosis tool for AI infrastructure (software & hardware).
• tempeh - tempeh is a framework to TEst Machine learning PErformance exHaustively which includes tracking memory usage and run time.

• GitHub Actions - Automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.
• Azure Pipelines - Automate your builds and deployments with Pipelines so you spend less time with the nuts and bolts and more time being creative.
• Dagli - framework for defining machine learning models, including feature generation and transformations as DAG.

• AI for Earth 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.

• Open Platfom for AI (OpenPAI) - resource scheduling and cluster management for AI.

  • OpenPAI Runtime - Runtime for deep learning workload.
  • OpenPAI Protocol - OpenPAI protocol enables job sharing and portability.
  • Openpaimarketplace - A marketplace which stores examples and job templates of openpai.
  • OpenPAI FrameworkController - built to orchestrate all kinds of applications on Kubernetes by a single controller.
  • HivedDScheduler - Kubernetes Scheduler for Deep Learning.
  • OpenPAI JS SDK - The JavaScript SDK is designed to facilitate the developers of OpenPAI to offer user friendly experience.
  • OpenPAI VS Code Client - Extension to connect OpenPAI clusters, submit AI jobs, simulate jobs locally, manage files, and so on.

• MLOS - Data Science powered infrastructure and methodology to democratize and automate Performance Engineering.

• Platform for Situated Intelligence - an open-source framework for multimodal, integrative AI.

• Qlib - an AI-oriented quantitative investment platform.

• Feast on Azure - Azure plugins for Feast (FEAture STore).
• Feathr - An Enterprise-Grade, High Performance Feature Store.

• TagAnomaly - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
• VoTT - Visual object tagging tool
• Satellite imagery annotation tool - A lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes.

• Visual Studio Code - Code editor redefined and optimized for building and debugging modern web and cloud applications.
• Gather - adds gather functionality in the Python language to the Jupyter Extension.
• Pylance - an extension that works alongside Python in Visual Studio Code to provide performant language support.
• Azure ML Snippets - VSCode snippets for Azure Machine Learning

• Microsoft AI for Earth

  • Shared utilities - A collection of utilities for working with Azure Machine Learning.
  • acoustic-bird-detection - Tutorial: Accurate Bioacoustic Species Detection from Small Numbers of Training Clips Using the Biophony Model
  • beluasound - Using machine learning to detect beluga whale calls in hydrophone recordings.
  • arcticseals - detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
  • AIDE: Annotation Interface for Data-driven Ecology - Detecting and classifying wildlife in aerial imagery.
  • Camera Trap Tool - tools for training and running detectors and classifiers for wildlife images collected from motion-triggered cameras.
  • Land cover mapping the Orinoquía region - A tool for predicting landcover in the Orinoquia region of Peru.
  • Planetary Computer Hub - a development environment that makes our data and APIs accessible through familiar, open-source tools, and allows users to easily scale their analyses.
  • Poultry barn mapping - code for detecting poultry barns from high-resolution aerial imagery and an accompanying dataset of predicted barns over the United States.
  • Planetary Computer SDK for Python - A Python SDK for the Planetary Computer Hub.
  • Species Classification - A tool for classifying species in images.

• News Threads - The News Threads project analyzes news articles to help find similarities between news articles and trace news provenance across time.

• InnerEye DeepLearning - Medical Imaging Deep Learning library to train and deploy models on Azure Machine Learning and Azure Stack

• Deep Seismic - Deep Learning for Seismic Imaging and Interpretation

• Multi-species bioacoustic classification - Multi-species bioacoustic classification using deep learning algorithms.

• Nestle Acne Assessment - deep learning models to assess the acne severity level based on selfie images.

• Visual Analogies - exploring the connections between artworks with deep "Visual Analogies".

• Forecasting Best Practices - time series forecasting best practices & examples.

• Computer Vision Recipes - best practices, code samples, and documentation for Computer Vision.

• DeepSpeed Examples - Example models using DeepSpeed

• A TALE OF THREE CITIES - Analyzing the safety (311) dataset published by Azure Open Datasets for Chicago, Boston and New York City using SparkR, SParkSQL, Azure Databricks, visualization using ggplot2 and leaflet.

• Microsoft Health Intelligence Machine Learning Toolbox - Microsoft Health Intelligence Azure Machine Learning Toolbox.

• Azure Machine Learning examples - official community-driven Azure Machine Learning examples, tested with GitHub Actions.

• Azure Machine Learning R Template - patterns and examples for running R code with Azure Machine Learning.

• Azure Machine Learning Python SDK notebooks - python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK.

• Azure Machine Learning Gallery - this repo enables our growing community of developers and data scientists to share their machine learning pipelines, components, etc. to accelerate productivity in the machine learning lifecycle.

• Azure Machine Learning previews - samples for preview features in Azure Machine Learning.

• AzureML Designer Sample - samples of Azure Machine Learning designer.

• Ship Detector - train an object detection model that can detect and locate ships in a satellite image.

• Microsoft Solution Accelerators

  • MLOps Solution Accelerator - this repository helps ML teams to accelerate their model deployment to production leveraging Azure.
  • Anomaly Detection Solution Accelerator - implement Anomaly Detection which is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations.
  • Classification Solution Accelerator - This is a classification solution accelerator to help you build and deploy a binary classification project.

• Medical Imaging with Azure Machine Learning Demos - medical imaging demo repository.

• Federated Learning in Azure ML - Examples and recipes around federated learning in Azure ML.

• Responsible AI Workshop - Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice.

• GlobalMLBuildingFootprints - Worldwide building footprints derived from satellite imagery.

• Genomics Data Analysis with Jupyter Notebooks on Azure - Jupyter Notebooks on Azure for Genomics Data Analysis.

• AI@Edge Community - find the resources you need to create solutions using intelligence at the edge through combinations of hardware, machine learning (ML), artificial intelligence (AI) and Microsoft Azure service.
• Global AI Community - empowers developers who are passionate about AI to share knowledge through events and meetups.
• Deep Learning Lab (Japan) - provides information on development cases and the latest technology trends related to deep learning.

• Workshop Wiki

• 2020 Machine Learning Security Evasion Competition - code samples for the 2020 Machine Learning Security Evasion Competition.

• PRML - Pattern Recognition and Machine Learning by Christopher Bishop
• Foundations of Data Science - 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
• Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions

• Microsoft Learn - Learning contents for Microsoft technology
  • Data Scientist, AI Engineer
• Data Science for Manager - Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions
• Github Learning Lab - learning contents for Github technology.
• Getting started with Python - Sample code for Channel 9 Python for Beginners course.
  • Python for Beginners
  • More Python for Beginners
  • Even More Python for Beginners
• Get started with PyTorch- learn the fundamentals of deep learning with PyTorch.
• Dev Intro to Data Science - In this 28-video series, you will learn important concepts and technologies to build your end-to-end machine learning applications on Azure.
• Machine Learning for Beginners - A Curriculum - 12 weeks, 24 lessons, classic Machine Learning for all
• Data Science for Beginners - A Curriculum - 10 Weeks, 20 Lessons, Data Science for All!
• Artificial Intelligence for Beginners - 12 Weeks, 24 Lessons, AI for All!
• Microsoft Cloud Workshop - Wide World Importers (WWI) delivers innovative solutions for manufacturers.

• channel9 - AI Show - videos for developers from people building Microsoft products and services.
• Microsoft Open Source Blog - blog about microsoft open source technology.
• Microsoft Research Event, Conference & Webinars - Events, Conferences & Webinars by Microsoft Research.
• Microsoft Innovation Tech Hub - AI project in Microsoft.
• LinkedIn Engineering Blog - Blog by LinkedIn Engineering Team
• AI System - system for AI Education Resource (Chinese).
• AI Edu - AI education materials for Chinese students, teachers and IT professionals (Chinese).

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