Near term
- machinedatahub.ai site live
- rebrand github group/repos to match
- Netlify Open Source plan application submitted
- License
- Code of Conduct at the top level directory of the project repository or prominently in the documentation (with a link in the navigation, footer, or homepage)
- Must feature a link to Netlify service
- Review that all conditions are met, fill out the form and submit
- machine-data-hub CLI does local ETL on at least one of the datasets
- Nested dataset schema
- each dataset can contain multiple files
- break out per file metrics vs. dataset metrics
- Dataset content pre-rendered, only the
- machine-data-hub published to PyPI
- unit testing runs on every push
- sphinx documentation pushes to readthedocs on tag
- library builds and pushes to PyPI on tag
- release notes section added to sphinx documentation
- Blog functionality added to web app
- blog content can be added to repo in markdown format
- create a welcome post
- Documented example of ML model built from a dataset
- UW ML Course students use machine-data-hub as data source for class project
- User trial with survey and reward to get feedback from potential users (possibly use to incentivize students above)
- Web App automated end to end testing
- Web app receives a 90+ rating from lighthouse for performance
- Auth-N (Authentication) implemented
- Up Voting datasets
- mitigation plan for duplicate votes (i.e. require Auth-N to cast a vote)
- machinedatahub analytics (page views, dataset download counts)
Longer term
- External user submits a new dataset
- First pull request merged from non-original team member
- Academic Paper Published
Maybe
- Auth-Z (Authorization) - allow private datasets