Imagimob Visual Graph UX Revolutionizes Machine Learning
Imagimob, an Infineon Technologies company, updates its Imagimob Studio. Users can now visualize their machine learning (ML) modeling workflows and leverage advanced capabilities to develop edge device models better and faster. The latest release of 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 modeling process while offering advanced new capabilities such as built-in data collection and real-time model evaluation for Infineon hardware.
Alexander Samuelsson, CTO of Imagimob, said, “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. Compared to our previous workflow which was more top-down with a fixed pipeline, visualizing 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. Moving over to the Graph UX and its capable ML backend is a big change for us and our users. We are taking a holistic approach to ML modeling which, in our industry, is unique.”
Imagimob Studio’s new Graph UX allows ML engineering teams to gain a complete visual overview of their modeling 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 modeling 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.
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.
