Edge computing company Adlink partnered with Intel and Amazon Web Services (AWS) on a product that targets industrial use cases that can benefit from artificial intelligence (AI) at the edge.

It combines Intel’s OpenVINO toolkit, which includes accelerators and streamlines deep learning workloads across Intel architecture, including accelerators, along with AWS Sagemaker and AWS Greengrass. Sagemaker is a fully-managed service for machine learning workflow, and Greengrass, extends AWS to edge devices so they can act locally on the data they generate while still using the cloud for management, analytics, and durable storage.

The product, called Adlink AI at the Edge, also comes integrated with Adlink Data River, which offers vendor-agnostic translation between devices and applications. And finally, it adds in the Adlink Edge software suite, which builds a set of deployable applications to communicate with end-points, devices, or applications. This software can also publish and/or subscribe to data topics on the Adlink Data River.

The product aims to make it easier for customers to design and deploy machine learning models by automating edge computing processes. The vendors claim the platform will allow industrial users to develop applications without needing advanced knowledge of data science and machine learning models.

“We’ve worked on multiple industrial use cases that benefit from AI at the edge, including a smart pallet solution that makes packages and pallets themselves intelligent so they can detect where they’re supposed to be, when they’re supposed to be there, in real-time,” said Toby McClean, VP of IoT innovation and technology at Adlink. “This enables warehouse customers to yield improved logistics and productivity, while also decreasing incorrectly shipped packages and theft. And this use case can be replicated across verticals to improve operational efficiency and productivity.”

Additional use cases include object detection modeling for object picking functions or worker safety, such as identifying product defects on conveyor systems or worker impediments in manufacturing environments, and equipment failure predictions to reduce machine downtime and increase productivity, the company says.