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· Sovont · 1 min read
The Model Registry Is Not Optional
Why every production ML team needs model versioning, eval tracking, and promotion workflows.
MLOps
“We just deploy from a notebook.”
Cool. Which version? Trained on what data? Evaluated against what baseline? Who approved it?
A model registry isn’t bureaucracy. It’s the difference between “we shipped a model” and “we can explain what’s running in production.”
At minimum, you need:
- Version tracking tied to training data
- Eval metrics stored alongside every artifact
- A promotion workflow — dev → staging → prod
- Rollback that takes seconds, not meetings
We’ve seen teams lose weeks debugging production issues that a registry would have caught in minutes.
MLOps isn’t about tools. It’s about knowing what you shipped and why.