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Nuxeo's UI levers AI

A new low-code user interface that opens AI to business users has been launched by Nuxeo (www.nuxeo.com).

With organizations struggling to unlock the potential of AI within their business, low-code user interface (UI) for Nuxeo Insight (www.nuxeo.com/products/ai) is Nuxeo’s artificial intelligence (AI) service that enables companies to use their own data and content to train custom machine-learning (ML) models.  The new interface will allow business users to easily create, train, deploy and administer personalized machine learning models in a point-and-click environment.

“For most organizations, custom machine learning models have remained the exclusive domain of data scientists.  However, with our entirely new, low-code interface, any organization can attain the same valuable business outcomes as those with AI experts,” explains Chris McLaughlin, Chief Product and Marketing Officer at Nuxeo.

A lack of AI capability is widely-acknowledged in many industries, and our own research reveals that 57% of UK Financial Services (FS) workers felt there is a shortage of AI talent and expertise in their organization.  Nearly 50% of respondents said that their organization’s inability to adopt AI quick enough was already impacting their day-to-day activities, while 13% said this was one of the main challenges facing UK FS in 2020.

“Success in AI is about more than technical talent.  We believe the secret is in understanding what specific business outcome you are designing for and then being able to intelligently select the right data and content to train your model for this outcome,“ says Eric Barroca, CEO at Nuxeo.  “We believe Nuxeo Insight is a powerful tool that we can place in the hands of business users to leverage AI to create new value for their organization.”

Nuxeo Insight’s new UI includes a dashboard that provides real-time updates on the training process and outcomes.  It also enables organizations to easily administer their custom ML models, allowing them to quickly promote new models into production and to actively monitor the performance of their models over time.

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