The developer-friendly workbench designed to help data scientists explore, search, and analyze visual data at large scale to accelerate model accuracy.
Virtually connect multiple data sources and associated meta-data databases to explore, search, and analyze visual data based on system-defined meta-data. Without the need to crete copies of data and meta-data, analysis can always be conducted with the most up-to-date versions.
Explore visual data on unlabeled datasets by combining traditional meta-data-based filtering with content feature-based latent structure exploration to better understand the inherent clustering or segmentation structure in the data set.
Perform powerful image-based similarity searches on millions of images in seconds and then further refine the results through interactive scoring on a subset of data to search for domain-specific features by combining active search techniques.
View model performance from multiple lenses and an aggregate view to identify the causes of inaccuracies within your model by isolating and focusing on the data that matters.
Identify novel data across multiple data sets to create more training points, evaluate labeling quality and consistency, determine data drifts, and more.
