Keep production models under watch
A model can keep running even after its inputs or predictions change in ways that matter.
- Industry
- Financial services example
- Goal
- Clear checks, ownership, and a response when performance changes
These are illustrative engagement examples. They show how we would approach a problem, not a claim about a specific client or measured result.
The situation
A financial services team has several models in use. Source data changes, a report of a bad prediction arrives, and it is hard to tell whether the issue is isolated or part of a broader shift. Infrastructure alerts show the systems are online, but say little about the quality of their decisions.
How we would help
We would agree with each team on what to measure, check that training and live use apply the same rules, and catch invalid inputs before they reach a model. Monitoring would track relevant changes in inputs, predictions, and outcomes where those outcomes are available. Each alert would point to an owner and a useful first check.
What the team would own
The result we would aim for is a repeatable way to review model health, investigate a problem, and decide when to fix, retrain, or leave a model alone. The client team would own the checks and could extend them to new models.
Have a problem like this?
Tell us what you are trying to improve. We can work out a useful first step together.