Marking Drasi’s first anniversary: introducing GQL support for Continuous Queries
Drasi turns one with GQL support—giving developers more ways to build change-driven systems.
Drasi turns one with GQL support—giving developers more ways to build change-driven systems.
For decades, fragments and unofficial copies of Microsoft’s 6502 BASIC have circulated online, mirrored on retrocomputing sites, and preserved in museum archives. Coders have studied the code, rebuilt it, and even run it in modern systems. Today, for the first time, we're officially releasing it under an open-source license.
Now, with Radius Resource Types, platform engineers can define resource types specific to their organizations.
The Azure Incubations team is proud to share that Drasi has officially been accepted into the Cloud Native Computing Foundation Sandbox.
With this practical guide, you now know how to secure your Kubernetes cluster using the structured-authentication feature, offering flexible integration with any JWT-compliant token provider.
We're announcing the release of Hyperlight Wasm: a Hyperlight virtual machine (VM) “micro-guest.
At Microsoft, we are committed to innovation in the cloud-native ecosystem through contributions and leadership from engineers across Azure.
Microsoft is experimenting with and investing in sustainability of the open source ecosystem sponsorships.
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.
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.
Mohit Ayani, Solutions Architect, NVIDIA Shang Zhang, Senior AI Developer Technology Engineer, NVIDIA Jay Rodge, Product Marketing Manager-AI, NVIDIA Transformer-based models have revolutionized the natural language processing (NLP) domain.
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.
In our previous blog, we spoke about the progress we have made for the eBPF for Windows project. A key goal for us has been to meet developers where they are. As a result, enabling eBPF programs written for Linux to run on top of the eBPF for Windows platform is very important to us.