Imagimob’s visual Graph UX machine learning modelling

The latest release of IMAGIMOB Studio—Imagimob’s development platform for AI/ML on Edge devices—includes the roll-out of a major user experience upgrade. The all-new Graph UX interface is designed to bring greater ease and clarity to the ML modelling process while offering advanced new capabilities such as built-in data collection and real-time model evaluation.

“What we’ve done here is represent ML projects in a way that makes more sense to users and gives them more freedom to operate,” says Alexander Samuelsson, CTO of Imagimob. “Compared to our previous workflow which was more top-down with a fixed pipeline, visualising the entire workflow as a graph allows for a better overview of your process while unlocking the possibility to combine and test things in new and different ways—and ultimately build better models and accelerate time to market.”

A vast improvement to the IMAGIMOB Studio UX

IMAGIMOB Studio’s new Graph UX allows ML engineering teams to gain a complete visual overview of their modelling canvas, with the ability to zoom in and out and work at different levels of complexity.

In addition to making it easier to work more efficiently as a team, Graph UX makes ML modelling projects more accessible to team members with varying skill sets and experience levels thanks to the ability to work at different levels of complexity and abstraction all in the same workspace.

Next-level capabilities and a brand new era for ML model design

IMAGIMOB Studio’s Graph UX update not only enhances user-friendliness but brings a collection of new capabilities to the ML design process. These include:

• Built-in data collection
• Real-time model evaluation
• Ability to evaluate and run multiple models in parallel or in sequence
• Post-processing of model outputs

Support for Infineon boards is now available right out of the box, and support for more hardware will be added soon. Users can also integrate their own hardware. Furthermore, Graph UX supplies a vital foundation for industry developments such as advanced models and hardware with multiple accelerators and processing units, like Infineon’s newly launched PSoC Edge.

“Moving over to the Graph UX and its capable ML backend is a big change for us and our users,” says Alexander. “We are taking a holistic approach to ML modelling which, in our industry, is unique.”

The first release of Graph UX covers Data collection, Evaluation, and Code Generation. In future releases, the entire end-to-end ML modelling process— including Data management, Data cleaning, Augmentation, and Training—will be available in the same unified workflow.