Client engagement · anonymised
Model Monitoring and Drift Detection for an Australian Logistics Operator
Model monitoring for an Australian logistics operator: metrics, drift detection and alerting with Prometheus and Grafana, live in four weeks.
- Industry
- Logistics
- Region
- Australia
- Focus
- Model monitoring & observability
Client engagement — the client is anonymised at their request; figures are as measured on the engagement.
Challenge
An Australian logistics operator of 100–200 staff relied on AI models in day-to-day operations but had no visibility into how they performed once deployed. There was no monitoring and no drift detection, so troubleshooting was reactive: problems were found when operations felt them, not when the models started to slip.
Solution
The approach: an observability layer around the production models that collects their metrics, detects drift, alerts the right people and shows model health on one executive dashboard — so the operator sees problems as they start, not after they have cost a shift.
Approach
- 1
We designed the monitoring architecture around the models in production and the operational decisions that depend on them.
- 2
We instrumented model metrics collection in Python, storing history in PostgreSQL and exposing live metrics to Prometheus.
- 3
We built a drift detection framework that compares live inputs and predictions against the data the models were trained on.
- 4
We set up alerting so the right owner is notified when a metric or drift signal crosses its threshold.
- 5
We built an executive dashboard in Grafana that shows model health at a glance, alongside detailed views for the technical team.
Frequently asked questions
What does drift detection look for?
Changes in the data a model receives, and in its predictions, compared with the data it was trained on. Drift is an early sign that accuracy may be about to fall.
Who receives the alerts?
Each alert goes to a named owner for that model, with enough context to decide what to do next, instead of a shared inbox nobody watches.
Why an executive dashboard as well as technical views?
Leaders need to know whether AI services are healthy without reading metrics. The executive view answers that at a glance; the technical views support diagnosis.