This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.
Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features? Learn more about the new capabilities that can assist you.
The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.
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.
Senior Program Manager, Azure Machine Learning
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.
Principal Program Manager, Banking and Capital Markets