Learn how to build an active learning program for continuous model improvement.
Active learning is a term that refers to various methods used for continuously improving the performance of your AI models with input data received even after your models are deployed to production. It relies on detector models and transfer learned classification models. In fact, transfer learning requires up to 10X less inputs to build than deep trained models. These models can also train 100X more quickly than deep trained models to help you get up and running more quickly and with greater accuracy.
If you're a data scientist, researcher, product innovator, AI operations director, or a data labeling lead then this webinar will be of benefit in supporting your enterprise’s digital transformation.
How to leverage Human-in-the-loop semi supervised annotation
How to build an active learning workflow using production data
A live use case demonstration on workplace safety and compliance
