Prioritizing inclusion—our commitment to building healthy open source communities
Microsoft products and services run on trust, an extension of our commitment to building healthy open source communities.
Menghua Xiao articles
Announcing preview of Microsoft build of OpenJDK for Java 11
Today we announcing the first preview of the Microsoft Build of OpenJDK, a new long-term support (LTS) distribution of OpenJDK that is open source and available for free for anyone to deploy anywhere.
Optimizing BERT model for Intel CPU Cores using ONNX runtime default execution provider
This blog was co-authored with Manash Goswami, Principal Program Manager, Machine Learning Platform. The performance improvements provided by ONNX Runtime powered by Intel® Deep Learning Boost: Vector Neural Network Instructions (Intel® DL Boost: VNNI) greatly improves performance of machine learning model execution for developers.
Microsoft Open Source success story—Babylon
An ongoing series of stories about Microsoft people and projects making their world better through open source. If you haven’t heard of Babylon.
Create privacy-preserving synthetic data for machine learning with SmartNoise
Watch our webinar on Open Data Science Conference Read the white paper on SmartNoise Differential Privacy machine learning case studies The COVID-19 pandemic demonstrates the tremendous importance of sufficient and relevant data for research, causal analysis, government action, and medical progress. However, for understandable data protection considerations, individuals and decision-makers are often very reluctant to share personal or sensitive data.
Enabling responsible AI development with new open source capabilities
At last year’s Microsoft Build conference in May 2020, Microsoft introduced three responsible AI (RAI) toolkits available in both open source as well as integrated within Azure Machine Learning: InterpretML, Fairlearn, and SmartNoise. These tools enable machine learning data scientists to understand model predictions, assess fairness, and protect sensitive data.
The community effort that delivered Dapr v1.0
Today we’ve announced the release of Dapr v1.0. The Distributed Application Runtime (Dapr) is now available as a production ready solution that helps developers build resilient, portable event-driven distributed applications on the cloud and edge.
Microsoft joins Rust Foundation
Today we’re excited to share that Microsoft is joining industry partners as founding members of the Rust Foundation, a new independent organization established to steward the Rust programming language and ecosystem and support Rust’s maintainers.
Maintainers Spotlight: How Kent C. Dodds pays it forward through open source
We hear about open source projects every day, but we rarely hear from the people who maintain them. Maintaining an open source project is a full-time, often thankless, job. The Maintainers Spotlight blog series is an opportunity to highlight the essential role maintainers play in moving projects and communities forward.
Deploying SUSE SAP HA Automation in Microsoft Azure
Why automation Maintaining a competitive advantage often depends on how quickly you can deliver new services. SAP applications are designed to help companies analyze data to anticipate new requirements and rapidly deliver new products and services. This gives you the ability to keep existing customers happy while attracting new business.
4 open source lessons for 2021
2020 fundamentally changed how many companies and teams work—seemingly overnight, remote-first cultures became the new norm and people had to change how they communicate and collaborate.
How to monitor your Azure infrastructure with Filebeat and Elastic Observability
Overview The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users.
Accelerate and simplify Scikit-learn model inference with ONNX Runtime
Scikit-learn is one of the most useful libraries for general machine learning in Python. To minimize the cost of deployment and avoid discrepancies, deploying scikit-learn models to production usually leverages Docker containers and pickle, the object serialization module of the Python standard library.