Keep your models accurate after launch
A model can start going out of date the day it launches. Model Operations watches how your models perform and retrains them as your market changes.
What is model operations?
Model operations, often called MLOps, is the ongoing work of monitoring and maintaining predictive models after they go live. It checks that they're still accurate and retrains them when your data or market changes.
Why models don't stay accurate on their own
A model learns from the past. When buyer behavior, your offers, or your marketing change, its predictions can slowly get worse, with no obvious sign of a problem.
Bad predictions can do quiet damage, because scores and values feed bidding, routing, and budgets. Regular monitoring helps catch a problem before it spreads through the rest of the loop.
What's included
Model performance and drift monitoring
Compare predictions with real outcomes, and watch for changes in your data that could make a model less reliable.
Ongoing retraining
Update models with new data on a regular schedule, or when monitoring shows they need it, and check each new version before it goes live.
How we approach model operations
1. Set the baseline
Record how each model performs at launch, so you have something to compare against.
2. Monitor continuously
Track accuracy and changes in the data the model relies on.
3. Retrain when needed
Update the model with newer data when performance slips or the market shifts.
4. Validate and release
Confirm the new version performs better before it replaces the old one.
How this connects to the loop
From Measurement
New outcomes show whether past predictions were right.
To Optimization
Reliable scores and values keep automated bidding and journeys pointed in the right direction.
Common questions
What is model drift?
Model drift is the gradual loss of accuracy that happens when the real world changes and no longer matches the data a model learned from.
How often should models be retrained?
It depends on how quickly your market and data change. Some models need a regular schedule, and others can be retrained when monitoring shows a drop.
How do we know a model is still working?
By comparing its predictions with what actually happened. If a lead scored highly and similar leads stopped converting, monitoring shows the gap.
What happens if a model's performance drops?
We find the cause, retrain or adjust the model, and check the new version before it goes live. The fix may also be in the data feeding it.
More in Prediction
- Scoring & PropensityEstimate who is likely to convert and what they may be worth over time.
- Value ModelingPut a value on leads and funnel stages so decisions reflect quality.
- ForecastingProject revenue and demand, and compare budget scenarios before you commit.
- Attribution & IncrementalityFind out what marketing really caused, using lift tests and mix modeling.
Talk to us about model operations
Tell us where your marketing stands today, and we'll show you what to do first.