The quality of input data is one of the most important factors in AI, machine learning, and data mining.
Building high-quality predictive models requires data which truly and comprehensively reflects the phenomenon that should be modelled or mined. AI algorithms typically do not detect implausible data on their own, but rely on the quality of data preselection and the quality of their labels. For predictive maintenance, for example, it may be necessary to label data from industrial processes as “good”, “critical”, “anomalous”, and so on, before building classification models. In practice, however, selection and labeling of data can be very time-consuming, difficult to address by scripted logic, and may require many iterations with domain experts.
Visplore reduces those challenges to a minimum – and saves you valuable time while improving the labeling results.