MLOps
Operationalizing ML: model versioning, deployment, monitoring for drift, retraining pipelines.
Why we need this / what value this brings
A model that isn't monitored will silently degrade (drift) without anyone noticing until outcomes get visibly worse.
When to use this
Once a model is in production and its predictions actually drive decisions.
How to use or implement this
Version models alongside the code, monitor prediction distribution over time, and have a defined retraining trigger.
Empty folder — drop notes, links, and findings here as you research.