Announcing ONNX Runtime 1.0
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.
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.
Microsoft has invested in confidential computing for many years, so I’m excited to announce that Microsoft will join industry partners to create the Confidential Computing Consortium, a new organization that will be hosted at The Linux Foundation. The Confidential Computing Consortium will be dedicated to defining and accelerating the adoption of confidential computing.
Congratulations! You’ve made it to the next installment of our overview of Trill, Microsoft’s open source streaming data engine. As noted in our previous posts about basic queries and joins, Trill is a temporal query processor. Trill works with data that has some intrinsic notion of time.
AzureR, a family of packages that provides tools to manage Azure resources from the open source R language, is now available. If you code in Python, C#, Java or JavaScript, you already have a rich selection of SDKs to choose from to interact with Azure.
This is part 2 of 2-post series that shows you how to use Trill, an open source .NET library designed to process one trillion events a day, for impression feedback.
On the Microsoft BingAds team, one of my primary responsibilities is the development and maintenance of the FastBI pipeline – the system responsible for all revenue coming from the Bing search engine.
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.
This post is the second in a sequence intended to introduce developers to the Trill streaming query engine, its programming model, and its capabilities. We introduced in the previous post the concept of snapshot semantics for temporal query processing.
Welcome to Data Accelerator! Data Accelerator for Apache Spark simplifies streaming big data using Spark. Data Accelerator has been used for two years within Microsoft for processing streamed data across many internal deployments handling data volumes at Microsoft scale.
Quantum computing is a new universe of computing that promises exponential increases in processing power, which could help scientists solve the problems of the future – on topics ranging from biomedical research and smart materials to cryptography and climate science.
At Microsoft, our mission is to empower every person and organization on the planet to achieve more. That’s why I’m happy to present the Autonomous Driving Cookbook which is now available on GitHub.