Interoperable Coding Practices and Design for Data Science Teams: With Differential Privacy as a first-class object
[x] Share Scikit learn models made in python in a python REPL (sharing the python's object sessions) Sharing that object into an R session, and see if one can make predictions based off the "serialized" python module.
[x] Working proof of concept of rpy2
[x] Document work being done in blog post and R/Python community feedback [(recieved top post on Rstats)]
[x] Try out pysyft and pytorch
[x] Build Atom-based IDE on top of radian (installed terminal and radian)
Objects with metadata: Attach an object's history so it can be accessed when transferred
Document requirements and dependencies in anticipation of creating a R/python virtual environment and/or docker (less priority)??
Publish pre-print on arxiv
Utilize apache arrow and parquet to serialize objects for in-memory and on-disk. This would help provide a way to bridge pandas and tidy dataframes.
Allow easier cloud connections (auth_file locations as environment variables that you have to log into) (single sign on ide)
Create a dedicated IDE for Federated Learning and Secure Model Communication In Healthcare setting?
Access objects from either an R or Python process when both are running.