Labeling data is a significant expense and bottleneck for Machine Learning (ML) and Natural Language Processing (NLP) development. Current approaches, such as manually labeling data through crowdsourcing and internal labeling efforts, carry significant drawbacks including high cost, extensive time and resource consumption, and low consistency and accuracy. Jaxon is a semi-supervised Training Data Platform (TDP) that uses deep learning to amplify a small number of human-provided labels into full-scale training datasets for text-oriented machine learning applications.

Jaxon brings together a number of open and proprietary techniques for effective sparse-data training. These techniques incorporate knowledge from large unlabeled corpora, human domain experts, and previously-trained models and machine learning assets in order to drastically reduce the demand for human labeling and annotation while improving model quality.

Jaxon continuously incorporates state-of-the-art technologies from the ML community, including deep learning architectures, classical algorithms, and the inclusion and ensembling of any custom logic already available (i.e. weak supervision). Successful machine learning is an iterative process that involves finding the right mixture of feature extraction, algorithmic selection, and parameterization to fit a specific combination of data and problem. Data science teams often try to build something like Jaxon, and waste a lot of time doing so. Let your data science team focus on model building and have Jaxon do the data prep.