Optimizing memory usage in large language models fine-tuning with KAITO: Best practices from Phi-3
The Cloud Native team at Azure is working to make AI on Kubernetes more cost-effective and approachable for a broader range of users.

The Cloud Native team at Azure is working to make AI on Kubernetes more cost-effective and approachable for a broader range of users.
We’re excited to share the recent integration of ONNX Runtime in Apache OpenNLP! Apache OpenNLP is a Java machine learning…
Together with our colleagues at LinkedIn, we are happy to announce that Feathr is joining the LF AI Data Foundation,…
Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are…
Welcome to KubeCon Europe 2022. While I am unfortunately stuck in rainy Seattle (coldest start to May in 20 years),…
Mohit Ayani, Solutions Architect, NVIDIA Shang Zhang, Senior AI Developer Technology Engineer, NVIDIA Jay Rodge, Product Marketing Manager-AI, NVIDIA Transformer-based…
Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the…
This post was co-authored by Jithun Nair and Aswin Mathews, members of technical staff at AMD. In recent years, large-scale…
In our previous blog, we spoke about the progress we have made for the eBPF for Windows project. A key…
Open source has forever changed software development for the better. It has allowed developers from around the world to connect,…
Today, we are excited to announce an open-source project called Azure AD workload identity for Kubernetes. It leverages the public preview…
Tech companies born with an open source mentality get it. It’s our ability to work together that makes our dreams…
ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the…