In summer 2019, I worked as a high school intern for the ONNX AI team at Microsoft and loved working on various projects with the team, including the BERT text classification model. However, due to Covid-19, the Microsoft Internship Program for high school students was canceled in the summer of 2020. This led two other Read more
From startups to the global 2000, Elastic powers search solutions for thousands of companies worldwide to find documents, monitor infrastructure, protect against security threats, and more. With Elastic Cloud managed services on Azure, you have the power of Elastic Enterprise Search, Elastic Observability, and Elastic Security. You can quickly and easily deploy as a managed Read more
ONNX Runtime is an open source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce ONNX Runtime release v1.5 as part of our AI at Scale initiative. This release includes ONNX Runtime mobile, a new feature targeting smartphones and other Read more
With the growing trend towards deep learning techniques in AI, there are many investments in accelerating neural network models using GPUs and other specialized hardware. However, many models used in production are still based on traditional machine learning libraries or sometimes a combination of traditional machine learning (ML) and DNNs. We’ve previously shared the performance Read more
Following the release of our Developer Preview in June, today we’re announcing an exciting next step as we make the source code of TensorFlow-DirectML, an extension of TensorFlow on Windows, available to the public as an open-source project on GitHub. TensorFlow-DirectML broadens the reach of TensorFlow beyond its traditional Graphics Processing Unit (GPU) support, by Read more
Model training is an important step when developing and deploying large scale Artificial Intelligence (AI) models. Training typically utilizes a large amount of compute resources to tune the model based on the input dataset. Transformer models, with millions and billions of parameters, are especially compute-intensive and training costs increase with model size and fine-tuning steps Read more
For Microsoft’s internal teams and external customers, we store datasets that span from a few GBs to 100s of PBs in our data lake. The scope of analytics on these datasets ranges from traditional batch-style queries (e.g., OLAP) to explorative ”finding the needle in a haystack” type of queries (e.g., point-lookups, summarization). Resorting to linear Read more
ONNX Runtime is an open source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. It is used extensively in Microsoft products, like Office 365 and Bing, delivering over 20 billion inferences every day and up to 17 times faster inferencing. Today we are introducing Read more
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). Due to the significant computation required, inferencing BERT at high scale can be extremely Read more
We are delighted to announce the Applied Cloud Stories initiative by Microsoft! What is Applied Cloud Stories? Do you work with open source? Are you passionate about machine learning or data science? Do you have stories to share about solving scale or data challenges? Are you investing time and effort so that you and your Read more
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. In the past few years, Read more
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. This release marks our commitment to API stability for the cross-platform, multi-language APIs, and introduces a breadth of performance optimizations, broad operator coverage, and pluggable accelerators to take advantage of new and exciting hardware Read more
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