Improve BERT inference speed by combining the power of Optimum, OpenVINO™, ONNX Runtime, and Azure 

5 min read

Make large models smaller and faster with OpenVino Execution Provider, NNCF and ONNX Runtime leveraging Azure Machine Learning. Read more

Faster inference for PyTorch models with OpenVINO Integration with Torch-ORT 

4 min read

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. Read more

Join Microsoft at KubeCon + CloudNativeCon Europe 2022 

3 min read

Welcome to KubeCon Europe 2022. While I am unfortunately stuck in rainy Seattle (coldest start to May in 20 years), I’m excited that many of our cloud-native Azure folks will be able to experience sunny Valencia. It’s especially exciting for KubeCon to be the first chance for parts of the Azure Kubernetes Service (AKS) team Read more

Optimizing and deploying transformer INT8 inference with ONNX Runtime-TensorRT on NVIDIA GPUs 

5 min read

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. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks Read more

Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime 

8 min read

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

Supporting efficient large model training on AMD Instinct™ GPUs with DeepSpeed 

6 min read

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