Discover the latest innovations at Azure Open Source Day 2023
Azure Open Source Day highlighted Microsoft’s commitment to open Source and focused on how Open Source Technologies can be used to build intelligent apps faster and with more flexibility.
Azure Open Source Day highlighted Microsoft’s commitment to open Source and focused on how Open Source Technologies can be used to build intelligent apps faster and with more flexibility.
Today, we are excited to announce the much-anticipated availability of the OSS Feathr 1.0.
On Azure, more than 50 percent of virtual machine (VM) cores run on Linux. There is no better time to learn Bash.
Azure Open Source Day is a great opportunity to learn more about Microsoft's role in the open-source community, its contributions, and vision.
The team at Pieces shares the problems and solutions evaluated for their on-device model serving stack and how ONNX Runtime enables their success.
Make large models smaller and faster with OpenVino Execution Provider, NNCF and ONNX Runtime leveraging Azure Machine Learning.
Many developers opt to use popular AI Frameworks like PyTorch, which simplifies the process of analyzing predictions, training models, leveraging data, and refining future results.
eBPF for Windows native code generation is a new mode of execution that maintains the integrity of the kernel and provides the safety promises of eBPF.
To celebrate FOSS Fund #25 we have invited all employees whose projects were not selected in past FOSS Fund to propose a project for a one-time $500.00 USD award. We expect this to result in over 50 projects receiving this microgrant for a total of $25,000 USD.
We’re excited to share the recent integration of ONNX Runtime in Apache OpenNLP! Apache OpenNLP is a Java machine learning library for natural language processing (NLP) tasks.
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.
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.