Monitoring AI in Production for Drift
An AI model that worked well at launch can quietly get worse over time as the world changes around it. This is called drift, and catching it requires ongoing monitoring.
This article explains drift and how we guard against it.
What Drift Is
A model learns from past data. When customer behaviour, products or conditions shift, the patterns it relies on no longer match reality, so its accuracy slips — often without any obvious warning.
How We Catch It
- Track accuracy and key metrics over time.
- Compare recent inputs against the training data.
- Sample outputs for human review.
- Set alerts when performance dips.
Keeping It Healthy
- Review performance on a regular schedule.
- Retrain on fresh data when drift appears.
- Treat monitoring as part of running cost, not optional.
Frequently Asked Questions
Is a model 'finished' once it launches?
No. Like any living system it needs monitoring and occasional retraining to stay accurate as conditions change.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.