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Neurala Unfurls AI App Builder in the Cloud

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Neurala announced it has made a self-service instance of the tool it created for building and training artificial intelligence (AI) applications available as a service on the Amazon Web Services (AWS) cloud.

Dr. Massimiliano Versace, CEO and co-founder of Neurala, says Brain Builder makes the tool Neurala created to simplify data tagging, training, deployment and analysis available as a cloud service as part of an effort to make AI accessible to a much broader range of organizations.

Brain Builder makes it possible for organizations to train an AI model as they tag and upload images, says Versace. Organizations can then choose to deploy that AI model in the cloud, in a local data center or at the network edge, adds Versace. Once deployed, that AI model continues to learn using a proprietary deep neural network (DNN) training methodology developed by Neurala, says Versace.

Neurala claims its Neurala AI engine has already been globally deployed in over 30 million devices. Common AI use cases for Neurala include AI applications involving drones, robotics, industrial inspections and devices such as smartphones, says Versace. Overall, Neurala claims its approach not only reduces the time required to build an AI model by 90 percent, the total cost is 25 percent less, largely because of the way it manages the images used to train the AI model.

The challenge now is to make the company’s AI engine accessible to organizations without requiring them to hire or contract a team of data scientists to create an AI application, says Versace.

“We absolutely fail if this tool winds up only be used by data scientists,” says Versace.

Neurala

It may be a little early to forecast the democratization of AI. But the day when only data scientists could build these applications is coming to an end. It will be much more commonplace very soon for business analysts to construct and train AI applications using a set of visual tools. Once that happens, the number of AI applications being deployed in production environments will naturally increase exponentially. In fact, the limiting factor will no longer be the AI skills that are available, but rather the quality of the data that can be relied on to create an AI model reliable enough to consistently automate a business process.

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