As of now, I think you could relatively easily wrap the plexe model, which has a `predict()` method, in a scikit-learn Estimator. You could then plug it into a Pipeline.
What do you have in mind? How would you want to use this with scikit-learn pipelines?
I.e. if I already have some data cleaning/normalisation, some dimensional reduction and then some fitting, being able to drop the Agent in place with an appropriate description and task.
Cleaning: Feed it a data frame and have it figure out what needs imputing etc.
The rest: Could either be separate tasks or one big task for the Agent..
You could wrap the Plexe-built model in a scikit-learn Estimator like I mentioned, and you can specify the desired input/output schema of the model when you start building it, so it will fit into your Pipeline.
This is an interesting requirement for us to think about though. Maybe we'll build proper support for the "I want to use this in a Pipeline" use case :)