The best ways to improve the accuracy of machine learning models are to increase the amount of labeled data ingested and/or re-label existing data. Deep Learning models in particular perform best with meaningful training datasets that contain millions to billions of examples for complex machine learning applications, and it takes months and massive amounts of manpower to get them labeled. By the time the data is labeled, it is frequently already outdated. Jaxon labels data in minutes, eliminating this bottleneck and allowing models to be updated continuously. With self-adjusting pipelines, Jaxon adapts to each organization’s nuanced data and domain-specific terminology. Training sets are created using existing data, as well as new text streaming in from online and internal sources. Jaxon’s Studio allows users to design and curate meta model(s), tune pipelines, and ensemble labelers to optimize training sets. With Jaxon, machine learning applications make more accurate classifications and predictions.