Enabling developer collaboration with Radius
Redefining how applications are build, managed, and understood with Radius—from the Azure Incubations team.
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Recommenders joins LF AI & Data as new Sandbox project
LF AI & Data Foundation announced Recommenders as its latest Sandbox project. -
Accelerating over 130,000 Hugging Face models with ONNX Runtime
ONNX models can be accelerated with ONNX Runtime, which works cross-platform and provides coverage for many cloud and language models. -
Boosting performance in ONNX Runtime with Intel® AMX for 4th Gen Intel® Xeon® Processors
ONNX Runtime harnesses Intel® AMX to accelerate performance for the 4th Gen Intel® Xeon® CPUs. -
Scaling workloads on Microsoft Azure with Kubernetes Event-Driven Autoscaling
KEDA reduces the complexity of infrastructure autoscaling, making it simpler to configure, manage, and secure the application auto-scaler. -
Introducing ONNX Script: Authoring ONNX with the ease of Python
ONNX Script is a new open-source library for directly authoring ONNX models in Python. -
Connect fluid dynamics, machine learning, and virtual reality with ONNX Runtime
Using ONNX Runtime to unlock the promise of developments in science for solving real world problems. -
On-Device Training with ONNX Runtime: A deep dive
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. -
Olive: A user-friendly toolchain for hardware-aware model optimization
Introducing Olive, an easy-to-use toolchain for optimizing models with hardware awareness. -
Automate optimization techniques for transformer models
Intel has collaborated with Microsoft to integrate Intel® Neural Compressor into Olive, enabling developers to easily take advantage of model compression techniques in their deployment platform, including Intel processors and accelerators. -
On-Device Training: Efficient training on the edge with ONNX Runtime
ONNX Runtime is a high-performance cross-platform inference and training engine that can run a variety of machine learning models. -
Kubernetes at scale with GitOps and Cluster API
As the requirements and software surrounding Kubernetes clusters grow along with the required number of clusters, the administrative overhead becomes overwhelming and unsustainable without an appropriate architecture and supportive tooling.
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