Visuals are Essential: Practical Collaboration Features for Advanced Projects in Machine Learning

Implicate all stakeholders, team members and domain experts with ease even if they can’t code.

A large-scale tensor displayed in Zetane, where the operator values are represented as a colour gradient. This is a close up of 25 million tensor components at 10 frames per second.

Making AI innovations for business or industrial processes requires collaboration between multiple stakeholders. To get an AI model right, data scientists need to collaborate with product mangers, non-technical domain experts, customers and others. It’s hard finding a universal language to discuss the technical details of data and AI with such diverse stakeholders — until now. Zetane provides several means to display data and its progression through a neural network.

Here is a collection of examples pertaining to clinical simulations, medical images and biochemical assessments. AI for biomedical applications requires careful input from clinicians and scientists to ensure proper data processing and to assess that the trained model focuses on clinically relevant features. The visual aspect of the workflow in Zetane makes it easy for these domain experts to monitor the process from start to finish.

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Get One-On-One Demonstrations: Choose From This List of Features, Datasets, Models and Case Studies