  Over the past few decades, the amount of scientific articles and technical
literature has increased exponentially in size. Consequently, there is a great
need for systems that can ingest these documents at scale and make their
content discoverable. Unfortunately, both the format of these documents (e.g.
the PDF format or bitmap images) as well as the presentation of the data (e.g.
complex tables) make the extraction of qualitative and quantitive data
extremely challenging. We present a platform to ingest documents at scale which
is powered by Machine Learning techniques and allows the user to train custom
models on document collections. We show precision/recall results greater than
97% with regard to conversion to structured formats, as well as scaling
evidence for each of the microservices constituting the platform.
