By thinking outside the box, we can envision creating a virtual multiverse. Within this innovative space, one can propose, evaluate, and decide on multiple hypotheses. Real-world examples of this approach include planning new product configurations, operating a plant, designing heating or cooling systems, or responding to catastrophes. Read more
Building upon the foundation we established earlier, this blog will present comprehensive information about the underlying details of training models directly on user devices using ORT. Equipped with these technical details, we encourage you to try out On-Device Training with ONNX Runtime for your custom scenario. Read more
ONNX Runtime is a high-performance cross-platform inference and training engine that can run a variety of machine learning models. ORT provides an easy-to-use experience for the AI developers to run models on multiple hardware and software platforms. Read more
As we come together in Amsterdam, there are significant headwinds and challenges facing us, but I’m confident that open-source and cloud-native computing are critical parts of the solutions. Read more
Today, we are excited to announce the much-anticipated availability of the OSS Feathr 1.0. Read more
The team at Pieces shares the problems and solutions evaluated for their on-device model serving stack and how ONNX Runtime enables their success. Read more
Make large models smaller and faster with OpenVino Execution Provider, NNCF and ONNX Runtime leveraging Azure Machine Learning. Read more
Together with our colleagues at LinkedIn, we are happy to announce that Feathr is joining the LF AI Data Foundation, an umbrella foundation of the Linux Foundation supporting open source innovation in AI and data. Read more
Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model. You can configure your environment and download Jupyter notebooks, but it would be nicer if there was a way to Read more
Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry. We’re happy to see that the ONNX Runtime Machine Learning model inferencing solution we’ve built and use in high-volume Microsoft products and services also resonates with our open source community, enabling new capabilities that Read more
This post was co-authored by Jithun Nair and Aswin Mathews, members of technical staff at AMD. In recent years, large-scale deep learning models have demonstrated impressive capabilities, excelling at tasks across natural language processing, computer vision, and speech domains. Companies now use these models to power novel AI-driven user experiences across a whole spectrum of Read more
ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime. ONNX Runtime is the open source project Read more
Microsoft Open Source Blog