In order to enable any AI-based solution, organizations need to perform comprehensive machine-training to build domain-specific AI models required for solving complex automation projects within an enterprise. This training is an expensive and time-consuming process comprised of selecting specific model types, preparation of training data and transfer learning.
TRAIN requires no manual-labelling or preprocessing of training data, rather, institutional contents are consumed as-is, with minimal manual curation.
TRAIN automatically labels large volumes of textual contents using semantic and language modelling techniques. Unsupervised long-
Using the Auto-Ontology feature, the users can now digitize their documents into a knowledge graph without the need of manually scripted ontologies. Along with the ontologies, TRAIN also produces a set of context models and language models used for building multi-label text classification schemes. The platform continuously learns from user feedback to improve the accuracy of its output. Easy to use training pipeline provides for validation by internal subject matter experts, allowing for knowledge blending between machine and expert.