Initially I had them implement REST wrappers around Apache OpenNLP and SpaCy and then compare the results of random data sets (project Gutenberg, sharepoint, etc..).

I ended up stealing a grad data scientist because we couldn't find a difference (while there was a difference in confidence, the actual matches were identical).

SpaCy required 1vCPU and 12GiB of RAM to produce the same result as OpenNLP that was running on 0.5 vCPU and 4.5 GiB of RAM.

2 grads were assigned a Spring Boot/Camel/OpenNLP stack and 2 a Spacy/Flask application. It took both groups 4 weeks to get a working result.

The team slowly acquired lockdown staff so I introduced Minio/RabbitMQ/Nifi/Hadoop/Express/React and then different file types (not raw UTF-8, but what about doc, pdf, etc..) for NLP pipelines. They built a fairly complex NLP processing system with a data exploration UI.

I figured I had a group to help me figure out Python best approach in the space, but Python limitations just lead to stuff like needing a Kubernetes volume to host data.

Conversely none of the data scientists we acquired were willing to code in anything but Python.

I tried arguing in my company of the time there was a huge unsolved bit of market there (e.g. MLOP's)

Alas unless you can show profit on the first customer no business would invest. Which is why I am trying to start a business.