Data Labeling for Enterprise AI & ML Experimentation
This whitepaper shows how more experiments & smaller volumes of data help high-performing AI teams build baselines quickly & rapidly iterate to improve.
This white paper identifies the applications most suitable to supervised verses unsupervised approaches and delineates how to create a sound data strategy.
Alegion's Data Science Team put together a whitepaper on annotation guidelines and improving the quality of labeling data. Download a complimentary copy.
Three key aspects to consider when choosing a video annotation platform for computer vision: Entity Persistence, Detecting State Change, Temporal Tagging.
AI & ML projects are still nascent according to Dimensional Research's survey of 277 data scientists and other AI professionals across nearly 20 industries
