Model Deployment
model-monitoring
The ongoing evaluation of a model's performance in a production environment.
Expanded definition
Model monitoring ensures that deployed machine learning models maintain expected performance levels over time. This includes tracking metrics such as accuracy, latency, and data drift. Regularly monitoring models helps identify when retraining or adjustments are necessary to adapt to changes in data or application requirements.
Related terms
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