  In this work, we propose the use of a fully managed machine learning service,
which utilizes active learning to directly build models from unstructured data.
With this tool, business users can quickly and easily build machine learning
models and then directly deploy them into a production ready hosted environment
without much involvement from data scientists. Our approach leverages
state-of-the-art text representation like OpenAI's GPT2 and a fast
implementation of the active learning workflow that relies on a simple
construction of incremental learning using linear models, thus providing a
brisk and efficient labeling experience for the users. Experiments on both
publicly available and real-life insurance datasets empirically show why our
choices of simple and fast classification algorithms are ideal for the task at
hand.
