Customers

With custom model training notebooks developed by Fritz, the Video Star team has been able to integrate the creation of new styles into their workflow. And with the ability to add tags and metadata to each custom style developed with Fritz, the team can easily manage their distribution.

David Hughes and his team set out to use machine learning to bring this expertise and knowledge into the hands (and smartphones) of farmers themselves. The result was PlantVillage’s Android app Nuru (meaning “light” in Swahili), which serves as an expert assistant for farmers in the field, detecting diseases in Cassava, potato, and African maize – all in real-time and without internet access.

To incorporate ML into MDacne, Dr. Harth and Oded turned to the Fritz team’s years of ML expertise in creating and integrating a proprietary, protected model to make these predictions on-device and in real-time.

To differentiate his photo editor from the rest, Pankaj focused on 2 specific components not commonly found in most photo editing mobile apps: masking and layering. These features allow users to let their imaginations run free – composite images, incredible double exposures, different worlds blended into one surreal landscape – directly inside the Superimpose X app.

[Momento] decided to implement a machine learning model that would segment out people and allow this dynamic interaction with AR effects. But the journey towards implementation turned out to be more difficult than expected.

Andrew and his team realized that to manage a growing library of user-generated content, they’d have to find a way to automatically do some of this work. For the kind of real-time verification One Bite needed, embedding an ML model in the app became an obvious solution.

With more than 15,000 app installs in the first couple of weeks, this meant he needed to monitor performance on a bunch of different devices and in different conditions. Without a team of developers at his disposal, Hart needed some help.