# What is Model Drift? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Operations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/model-drift](https://spidits.com/ai-glossary/model-drift)

## Definition
Model Drift (or model decay) is the degradation of an AI model's predictive performance in production over time, caused by changes in the statistical properties of real-world input data relative to the training data.

## Architectural Mechanics & Deep Dive
Model drift is the degradation of a model's predictive performance in production over time due to changes in real-world data distributions (data drift) or the underlying relationships between inputs and targets (concept drift). Mitigating model drift requires continuous monitoring, anomaly alerts, and periodic retraining on fresh datasets.

## Industry Applications & Real-World Use Cases
MLOps model tracking, prediction monitoring, and automated retraining pipelines.

## Frequently Asked Questions

### What is concept drift vs. data drift?
Data drift (covariate shift) is when input distributions change (e.g. users search for new terms). Concept drift is when the relationship between inputs and targets changes (e.g. consumer preferences shift, making old classification rules obsolete).

### How is model drift resolved?
By continuous monitoring in MLOps, setting up anomaly alerts on input distributions, and automatically triggers retraining pipelines on fresh production data.

## Related Terminology
- [mlops](https://spidits.com/ai-glossary/mlops)
- [observability](https://spidits.com/ai-glossary/observability)
- [inference](https://spidits.com/ai-glossary/inference)
- [validation-data](https://spidits.com/ai-glossary/validation-data)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*