We're passionate engineers who want to make deep learning more efficient. We're making use of representation learning using self-supervised methods to understand raw data. Our solution can therefore be used before any data annotation step. The learned representations can be used to analyze and visualize your datasets as well as for selecting a core set of samples that can be used for further steps in the data preparation pipeline. Our active-learning library is powered by the same algorithms to help you with iterative active-learning loops.
Preparing and organizing data for machine learning has never been easier. With our platform anyone can become a data preparation engineer. Visual feedback helps you understand which samples are within your datasets and which have been removed. Keep track of different datasets versions using tags. Collaborate with your team in data cleaning and share the final datasets with your ML engineer training and evaluating models.