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.
- Assess existing state of code and results
- Notebooks and existing git repository.
- Usage by external teams and their points of interaction.
- Convert to reproducible format
- Convert code to time-boxed chapters or modules.
- Create an upstream version of the project with sample data.
- 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