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 と Microsoft Azure は、AI のディープ ラーニング機能、コンピューター ビジョン、音声やスピーチの機能など、インテリジェントな IoT 技術やサービスをデプロイできるよう、連携して企業を支援しています。これらの機能を追加することで、ソリューションではより多くのビジネス上の課題を解決することができます。また 2 つ以上のものを組み合わせることで、たとえばコンピューター ビジョンと AI の両方を追加することで、IoT ソリューションの潜在的な用途を大きく広げることができます。
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.
The continuous monitoring of health metrics is a fundamental part of this process, and this is where AIOps plays a critical role. In the post that follows, we introduce how AI and machine learning are used to empower DevOps engineers, monitor the Azure deployment process at scale, detect issues early, and make rollout or rollback decisions based on impact scope and severity.
Today, Azure announces the general availability of the Azure ND A100 v4 Cloud GPU instances—powered by NVIDIA A100 Tensor Core GPUs—achieving leadership-class supercomputing scalability in a public cloud. For demanding customers chasing the next frontier of AI and high-performance computing (HPC), scalability is the key to unlocking improved total cost of ownership and time-to-solution.
Our commitment to developers is to make Azure the best cloud for developing intelligent applications that harness the power of data and AI. At Microsoft Build, we are announcing several exciting new capabilities and offers that make it easy and cost-effective for developers to get started with Azure data and AI services.