With the advance of AI and machine learning, companies start to use complex machine learning pipelines in various applications, such as recommendation systems, fraud detection, and more. These complex systems usually require hundreds to thousands of features to support time-sensitive business applications, and the feature pipelines are maintained by different team members across various business groups.
In June 2021, Microsoft and AT&T reached a major milestone when we announced an industry-first collaboration to adopt Microsoft cloud technology for AT&T’s 5G core network workloads. Since then, we have had requests from many operators, partners, and customers to share more details. This blog is intended to do just that.
In the year since Azure Space was announced, Microsoft has focused on extending its Azure Space ecosystem by partnering with industry leaders in the space community to empower customers and partners to achieve more. Today, Microsoft is expanding on its mission to make Azure Space the platform and ecosystem of choice for the space community through a new partnership with Airbus and the general availability of their premium satellite imagery and elevation data in Microsoft’s Azure Maps.
AI is transforming business and the world. However, AI models learn from the data. Biases that exist in society will exist in the models. Human judgment must be the overriding factor, ensuring that AI models benefit and are inclusive of everyone. Equally important, AI must inspire trust in customers that their data is being used appropriately. These are key reasons that responsible approaches to AI are so critical, and you can learn how to put responsible AI into practice.
Due to the complexity, high cost of operations, and unscalable infrastructure, on-premises Hadoop platforms have often not delivered on their initial promises to impact business value. As a result, many enterprises are now seeking to modernize their Hadoop platforms to cloud data platforms.
October ushers Cost Management and Billing into a new world – from coverage of Microsoft 365, Dynamics 365, and more to a new tool that helps you reduce cost and manage your on-prem licenses. You can also update your address or purchase order number on existing invoices or try an early preview of subscription cost anomaly detection. All this plus 12 new cost-saving options, 3 new videos, and 7 documentation updates. We hope you're as excited about what the future holds as we are!
From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming. That’s why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI.
A growing number of organizations are taking advantage of machine learning to increase efficiency, enhance customer experiences and drive innovation. Azure Machine Learning is the enterprise-grade service to build and deploy models faster and accelerate the machine learning lifecycle.
Intel and Microsoft Azure are working together to help enterprises deploy intelligent IoT technologies and services, including AI’s deep learning abilities, computer vision, and audio or speech capabilities. Adding these capabilities enables solutions to solve more business challenges, uniting two or more—adding both computer vision and AI, for example greatly expands the potential uses for IoT solutions.
Today, AI and machine learning are enabling data-driven organizations to accelerate their journey to insights and decisions. With all the latest advancements, AI is no longer limited to only those with deep expertise or a cache of data scientists, and many organizations can now adopt AI and machine learning for better competitive advantage. Customers with analytics practices looking to adopt machine learning can read this report to get started.