Why does everyone need to develop a machine learning package?
summary
Claims that building an internal ML package yields tested, reusable pipelines and business value backed by concrete work: team authors Aetna, moves from proof-of-concept to open source over about a year, iterates architecture refactored architecture three times, replaces bespoke scripts with YAML config + Docker image, adds unit tests CI/CD linters and poetry for packaging, eliminates a god-object DataHandler, gains unified logging and onboarding docs and even accrues community traction (about 649 GitHub stars) while cutting time to production.
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chapters (8)
- 0:00Introduction
- 0:53What are packages
- 2:57How to make a package
- 9:00Architecture
- 13:46Open Source
- 15:50Documentation
- 16:24GitHub
- 18:02Conclusion
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