Shipping machine learning systems requires as much attention to process as it does to models. The best paper results rarely survive the handoff to a product environment without deliberate engineering.
Make uncertainty visible
- Track experiment lineage with ruthless detail.
- Carry evaluation metrics into dashboards that the entire team can see.
- When a model surprises you, write down why in plain language.
Research debt grows silently. Naming assumptions in public keeps teams honest.
Treat datasets like products
The data work that happens before training deserves product-level polish. Write changelogs, publish schemas, and give feedback pathways to the people closest to the data.
for version in dataset.versions():
summary = evaluate(version)
if summary.drift > threshold:
page_team(summary)Communicate in both directions
Research reviews should end with pull requests, and product reviews should include a quick dive into the model's health. Build rituals that keep curiosity and pragmatism in the same room.