Microsoft Weekly Data Science News for May 12, 2018
It was a big week for Microsoft due to the Microsoft Build Live conference. Tons of exciting announcements for developers. Videos here, Build 2018 on YouTube.
- Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework – ML.NET also complements the experience that Azure Machine Learning and Cognitive Services provides by allowing for a code … needed for all aspects of machine learning, including core data types, extensible pipelines, high performance math, data …[Read More]
- Microsoft empowers developers with new and updated Cognitive Services – Today at the Build 2018 conference, we are unveiling several exciting new innovations for Microsoft Cognitive Services on Azure. At Microsoft, we believe any developer should be able to integrate the best AI has … of-the-art machine learning neural …[Read More]
- AI and the New Generation of Software Building Blocks – From AI, to mixed/virtual reality, to blockchain, to IoT … of data at scale, and breakthroughs in AI research that are powering access to easier to use tools and techniques. While Microsoft Research has been working on AI and deep learning techniques …[Read More]
- Azure Stack: the last mile in Hybrid Cloud – These include Microsoft Azure Cognitive Services, exceptionally large HDInsight environments, and Microsoft Azure Data Lake Store. Services which are best consumed in a Hyperscale Cloud will run on Azure, while services that best fit an enterprise …[Read More]
- Make Your FAQ Bot Application By Simple Clicks – but now part of Microsoft Research’s Cognitive Services – previously “Project Oxford” – a collection of extremely powerful machine learning APIs for processing images, video, text, speech, to extract meaning. As a simple bot application let’s …[Read More]
- Tip o’ the Week 430 – developers, developers, developers – The cloud platform in Azure can take data from devices on the edge and process it on their behalf, or using smarter devices, do some of the processing locally, perhaps using machine learning models that have been trained in the cloud but executed at the edge.[Read More]