End-End Use Case for AI Enablement Initiative

The final output of this intern project would be an end to end use case which will work as a standard for enabling teams to implement data science projects in production.

This will result in converting an existing data science project into a consumable and reproducible structure, and converting the findings of the data science project into an understandable and discoverable format as a POC for the AI Enablement Initiative. The artifacts and results will be published in a common portal and the usage of the project will be tracked.

  1. Assess existing state of code and results
  • Notebooks and existing git repository.
  • Usage by external teams and their points of interaction.
  1.  Convert to reproducible format
  • Convert code to time-boxed chapters or modules.
  • Create an upstream version of the project with sample data.
  1. Advertise artifacts and results
  • Use a tool to generate reports from a notebook which can be converted into blogs that will be hosted in a section in help.datahub.redhat.com.
  • Blog post on how to engage with code.
  • Integrate upstream repo with mybinder.org

Red Hat Intern: Rajat Tripathi


Project Resources