MODEL GOVERNANCE
Model drift monitoring: update a model when it needs it, not on a calendar
Model drift monitoring with TIMi shows when a predictive model starts to lose reliability because the behaviors or conditions it learned have changed, using the Drift Report built into Modeler.
Drift Report
Built into Modeler, with no separate monitoring tool to build.
Over time
Compare how a model behaves now with how it was built.
A basis for decisions
An objective signal to decide when to update.
HOW IT WORKS
How model drift monitoring works.
Three steps, in one engine, on a standard server with no cluster.
1. Train and document
Modeler trains the model and documents its variables and performance at training time, which becomes the reference.
2. Compare over time
The Drift Report evaluates the validity of the model against the evolution of the data, as new data comes in.
3. Decide
When the report shows a shift, you decide whether to retrain or to review the model. At SANY, it made a seasonal shift visible right away.
ModelerDrift ReportGETTING IT RIGHT
What a drift signal means.
A model that was accurate on day one can drift silently.
A reason to look again, not an order to replace
A drift signal means a review is needed. Depending on its size and cause, a simple retraining may be enough, or a deeper review of the model may be required.
Evaluate at the pace of your data
The evaluation can follow the frequency at which new data arrives, depending on the context of the model concerned.
Use real outcomes when you have them
If the actual results are observed afterwards, they can confirm or contradict past predictions and make the review more precise.
EXPLAINABILITY
A decision to update should rest on evidence.
Many organizations discover the drift of a model afterwards, when its decisions visibly start to be wrong. Following drift as part of the model itself helps to anticipate that moment instead of observing it, without depending on a periodic manual check or a separate monitoring tool.
For data and business teams
The decision to update a model rests on an objective signal, rather than an arbitrary calendar or a doubt after the fact.
For model governance
A model whose drift is followed is simpler to audit and to justify over time. Whether this meets AI Act obligations stays with your legal and compliance teams.
Across use cases
The same report applies to the models generated in Modeler, for churn, maintenance, fraud or forecasting alike.
DATA
What data drift monitoring needs.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Training data
The reference for the model.
New data in production
The continuous flow the model is applied to.
Observed outcomes
If available, to confirm or contradict past predictions.
Nothing extra
No data beyond what already feeds the model.
PROOF
Drift monitoring in production.
SANY is our reference for drift monitoring.
A seasonal decay made visible
Models built in summer decayed in winter because the thermal baseline changed. The Drift Report visualized the shift, so engineers could adapt quickly.
FAQ
Common questions.
Do we need a separate tool or setup?
No. The Drift Report is built into Modeler, with no external monitoring tool to put in place.
How often is drift evaluated?
The evaluation can follow the frequency at which new data arrives, depending on the context of the model concerned.
Does a drift signal mean the model must be replaced right away?
Not necessarily. It means a review is needed. Depending on the size and cause of the drift, a simple retraining may be enough, or a deeper review of the model may be required.
Does it work for all the models produced by Modeler?
Yes, the principle applies to the models generated by AutoML in Modeler, whatever the use case.
RELATED USE CASES
Keep exploring.
Seasonal Model Recalibration
Adapt a model to predictable seasonal changes without rebuilding it each cycle.
Predictive Maintenance
Anticipate equipment failures from the data you already collect, with the reasons.
Churn Prediction
Identify which customers are likely to leave, and why.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
