How to build AI data engines that use the right data at the right time

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Machine learning (ML) has broad applications -- and supervised ML, particularly, has taken off in recent years. Thus, it's critical that organizations build data engines that utilize the right data at the right stage of their projects' lifecycles, Manu Sharma told the audience at VentureBeat's Transform 2022 event. The founder and CEO of Labelbox explained that the "fundamental premise" of supervised ML is creating annotated or labeled data. This involves applying semantic annotations on any unstructured information, such as text and video.