2019年7月3日
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.
2019年6月6日
Automated Machine Learning の機能のリリースにより、予測の精度を向上できます。トレーニング データにギャップが必要なシナリオ、予測の精度を向上させるためにコンテキスト データを適用する必要があるシナリオ、特徴量にラグを適用する必要があるシナリオに役立つ新機能の詳細について説明します。
2019年6月4日
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.
2019年5月9日
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.
2019年5月3日
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.
2019年3月26日
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.
2019年2月11日
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.
2019年1月28日
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.
2018年12月17日
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).
2018年10月9日
Combining biometric identification with artificial intelligence (AI) enables banks to take a new approach to verifying the digital identity of their prospects and customers.
