Artificial intelligence (AI) has become the hottest topic in tech. Executives, business managers, analysts, engineers, developers, and data scientists all want to leverage the power of AI to gain better insights to their work and better predictions for accomplishing their goals.
With the exponential rise of data, we are undergoing a technology transformation, as organizations realize the need for insights driven decisions. Artificial intelligence (AI) and machine learning (ML) technologies can help harness this data to drive real business outcomes across industries. Azure AI and Azure Machine Learning service are leading customers to the world of ubiquitous insights and enabling intelligent applications such as product recommendations in retail, load forecasting in energy production, image processing in healthcare to predictive maintenance in manufacturing and many more.
Along with the general availability of Azure Data Box Edge that was announced today, we are announcing the preview of Azure Machine Learning hardware accelerated models on Data Box Edge.
Drilling for oil and gas is one of the most dangerous jobs on Earth. Workers are exposed to the risk of events ranging from small equipment malfunctions to entire off shore rigs catching on fire.
As part of our ongoing commitment to open and interoperable artificial intelligence, Microsoft has joined the SciKit-Learn consortium as a Platinum member and released tools to enable increased usage of SciKit-Learn pipelines.
In the natural language processing (NLP) domain, pre-trained language representations have traditionally been a key topic for a few important use cases, such as named entity recognition (Sang and Meulder, 2003), question answering (Rajpurkar et al., 2016), and syntactic parsing (McClosky et al., 2010).
Combining biometric identification with artificial intelligence (AI) enables banks to take a new approach to verifying the digital identity of their prospects and customers.
In the past two years since PyTorch's first release in October 2016, we've witnessed the rapid and organic adoption of the deep learning framework among academia, industry, and the AI community at large.
We are pleased to announce public preview refresh of the Azure Machine Learning (AML) service. The refresh contains many new improvements that increase the productivity of data scientists.
Today we are very happy to release the new capabilities for the Azure Machine Learning service. Since our initial public preview launch in September 2017, we have received an incredible amount of valuable and constructive feedback.