Hi guys! I’m tasked with designing a data generation and management pipeline for an upcoming machine learning project, and I want to make sure our approach is scalable from day one rather than just relying on ad-hoc script writing. I was reading a technical breakdown on https://snorkelorg.com/ recently about shifting the focus from static model tuning to programmatic training data creation and iteration. If you’ve built custom dataset generation systems before, what architectural patterns or libraries do you find indispensable for keeping your labeling functions and data versions organized?
