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Sponsored: Leverage Data Processing at the Edge

September 29, 2020

The ability to perform data science at the edge is growing increasingly important for organizations across the public sector. From smart traffic cameras to hospitals using data processing for faster diagnosis and warfighters leveraging data in theater, the need to derive actionable intelligence at the edge has never been greater.

Gartner researchers predict that by 2025 three quarters of enterprise-generated data will be created and processed at the edge, outside of a traditional data center or cloud. Fulfilling the promise of real-time edge data processing and analysis requires significant intelligence and computational horsepower that’s close to the action.

While inferencing at the edge is becoming more common, large-scale artificial intelligence (AI) and machine learning workloads still are not typically completed there. Instead, they are sent back to a core data center for processing, adding significant time to achieve results.

Real-time edge processing is historically a logistical challenge and a time-consuming process—particularly for large-scale organizations. Getting the insights they need may require hundreds of teams managing hundreds of solutions, leading to inconsistencies, inefficiencies, and technical debt.

Real AI Power at the Edge

Wouldn’t it be nice if you could have the power of a data center in a compact, portable system? Now you can.

KubeFrame for AI-Edge is a turnkey, field-deployable solution that combines Red Hat OpenShift Container Platform with Hewlett Packard Enterprise Edgeline EL8000 Converged Edge System and NVIDIA processing. Compact design and engineering make the solution easily transportable. It’s pre-engineered for just about any field environment and can be stood up in under 60 minutes. Housed in a case that can easily fit in an airplane’s overhead compartment, the kit can be transported to, and quickly installed, in an almost unlimited variety of field locations. Users can leverage the same tools they already know without the need to learn anything new, eliminating the need for specialized solutions.

With processing at the edge, the volume of data is significantly optimized, reducing the need to create new data pipelines for core-based processing. The solution combines easily portable hardware, powerful open source software and industry-leading security to make real-time AI-driven edge processing a reality for mission critical success.

Built with Customers in Mind

Customers benefit from three technology giants—Red Hat, Hewlett Packard Enterprise and NVIDIA—coming together to create highly secure data processing at the edge. Bringing these hardware and software vendors together ensures that this best-of-breed solution is better designed to address the mission itself than a one-size-fits-all full stack from one vendor.

With KubeFrame for AI-Edge, organizations can run applications they thought were limited to the data center and transport them to where the data actually is. Public sector customers that examine imagery can now perform inspections in the field, allowing better command and control of engagement and safety decisions.

AI at Your Fingertips

Any workload, any footprint, any location. With KubeFrame for AI-Edge, organizations are extending data center capabilities to the edge itself. End users gain operational simplicity in bringing a consistent platform to each location.

Customers can tailor it to their environment, run it in a disconnected environment, and enjoy performance that wasn’t possible before. This collaborative solution has solved the issues that prevented customers from experiencing meaningful enterprise compute, enterprise security, and enterprise management at the edge.

Thanks to its flexible open source underpinnings, KubeFrame for AI-Edge is a cloud-neutral, vendor-agnostic system. Essentially an Edge Server that connects intelligent devices and any and all cloud services, the solution is built to execute high-performance GPU-enabled AI and machine learning tasks in an extremely portable form factor.

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