Interoperable Coding Practices and Design for Data Science Teams: With Differential Privacy as a first-class object
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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.
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Working proof of concept of rpy2
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Document work being done in blog post and R/Python community feedback [(recieved top post on Rstats)]
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Try out pysyft and pytorch
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Build Atom-based IDE on top of radian (installed terminal and radian)
- Make downloading and setup 1 click
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Objects with metadata: Attach an object's history so it can be accessed when transferred
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Document requirements and dependencies in anticipation of creating a R/python virtual environment and/or docker (less priority)??
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Publish pre-print on arxiv
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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.
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Allow easier cloud connections (auth_file locations as environment variables that you have to log into) (single sign on ide)
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Create a dedicated IDE for Federated Learning and Secure Model Communication In Healthcare setting?
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Access objects from either an R or Python process when both are running.