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Siemens Digital Industries Software and SAS announced a new partnership today that will help companies create new IoT edge and cloud-enabled solutions by applying SAS and open source streaming analytics through Siemens’ MindSphere. Users will gain access to industry-leading SAS advanced and predictive analytics in MindSphere, which can accelerate the adoption of machine learning (ML) and artificial intelligence (AI) in Internet of Things (IoT) environments. Increasing productivity and reducing operational risk through powerful predictive and prescriptive maintenance and optimized asset performance management are just some of the ways these new solutions can benefit customers working in a range of industries including manufacturing, healthcare, energy & utilities, smart cities, transportation and automotive.

Siemens and SAS will collaborate to engage with new and existing customers and, beginning with streaming analytics, enable near-real-time embedded AI for IoT devices at the edge. The partners expect the solutions to be generally available later this year.

“SAS is a recognized world-leader in advanced analytics, machine learning, and artificial intelligence. We are excited to leverage their analytics in MindSphere,” said Stephen Bashada, Executive Vice President and General Manager of Siemens MindSphere. “The combination of Siemens’ deep industrial domain knowledge with SAS’ deep analytics knowledge is a powerful step forward for IoT.”

By intuitively applying AI and operationalizing its potential at scale, the partnership can drive a world class end-to-end solution framework for customers. Companies currently using both SAS and MindSphere will be able to port and deploy previously developed SAS models natively into MindSphere while new users will gain access to powerful analytics capabilities.

“Siemens’ pedigree in innovative operational assets, software and processes is unmatched. Their commitment to digitizing the world’s industries provides a unique platform for IoT to realize its full potential through AI,” said Jason Mann, Vice President of SAS’ IoT Division. “Our partnership can accelerate adopting the transformative value of IoT for our customers.”

Siemens’ MindSphere is the cloud-based, open IoT operating system that connects real things to the digital world through open connectivity. It enables powerful industry applications and digital services to drive business success. MindSphere also enables a rich partner ecosystem to develop and deliver new applications providing a basis for new business models. With its rich APIs, MindSphere applications can be quickly and easily developed by Siemens, its partners, or directly by customers. In combination with Siemens’ industry-leading approach to holistic digital twins, companies can leverage MindSphere to close the loop through product ideation, realization and utilization to seamlessly integrate IoT data throughout the value chain –driving operational efficiency and innovation.

SAS AI and IoT technologies support diverse environments and scale to meet changing business needs — for IoT data at the edge or in the cloud, in motion or at rest. With SAS IoT Analytics solutions, business and technology leaders can understand machine operational and behavioral patterns, develop fast and accurate predictions, and make optimal decisions with greater confidence while reducing data movement, latency and storage costs.

SAS advanced analytics algorithms capture and analyze large amounts of data gathered from industrial control systems and converge IT and OT worlds by using derived actionable insights to drive intelligent operational and business processes. SAS’ investment in IoT analytic open source compatibility allows data scientists to code in their language(s) of choice while relying on the resiliency and comprehensive scalability of SAS. By fundamentally changing the way IT and OT handle data and extract insights, customers can see patterns and trends they would never have seen otherwise. SAS IoT Analytics with embedded AI is a key differentiator for customers to unlock real value from data.

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