Content Drift in AI
One of the biggest challenges we face building AI solutions isn't building the models but instead, it's keeping it accurate over time.
Concept drift occurs when the relationship between the data and the correct outcome change over time. In other words the patterns the model originally learned are no longer true.
For example, an AI system trained to predict whether a property seller is likely to move might perform well today. But if interest rates rise, legislation changes, or buyer behaviour shifts, the indicators of someone looking to sell may change. Unless the model is retrained, its predictions will gradually become less reliable.
To combat concept drift, organisations should:
- Monitor model performance continuously.
- Detect changes in prediction accuracy.
- Retrain models with recent data.
- Validate updates before deploying them to production.
- AI models aren't "train once and forget."
Like any software system, they require ongoing maintenance to continue delivering reliable results. Understanding and managing concept drift is essential for building AI systems that remain accurate in the real world. "Keeping the lights on" tasks within software engineering are typically pushed to the bottom of a backlog to be forgotten about, but we can not adopt that strategy when maintaining AI systems.