This post is co-authored by Emma Ning, Azure Machine Learning; Nathan Yan, Azure Machine Learning; Jeffrey Zhu, Bing; Jason Li, Bing One of the most popular deep learning models used for natural language processing is BERT (Bidirectional Encoder Representations from Transformers).
Today the Open Neural Network eXchange (ONNX) is joining the LF AI Foundation, an umbrella foundation of the Linux Foundation supporting open source innovation in artificial intelligence, machine learning, and deep learning. ONNX was co-founded by Microsoft in 2017 to make it easier to create and deploy machine learning applications.
One year after ONNX Runtime’s initial preview release, we’re excited to announce v1.0 of the high-performance machine learning model inferencing engine.
ONNX Runtime 0.5, the latest update to the open source high performance inference engine for ONNX models, is now available. This release improves the customer experience and supports inferencing optimizations across hardware platforms. Since the last release in May, Microsoft teams have deployed an additional 45+ models that leverage ONNX Runtime for inferencing.
Organizations that want to leverage AI at scale must overcome a number of challenges around model training and model inferencing. Today, there are a plethora of tools and frameworks that accelerate model training but inferencing remains a tough nut due to the variety of environments that models need to run in.
