Model Monitoring
Tracking model performance, data distribution, and predictions in production to detect issues and degradation.
Related Concepts
- MLOps: Explore how MLOps relates to Model Monitoring
- Production: Explore how Production relates to Model Monitoring
- Data Drift: Explore how Data Drift relates to Model Monitoring
- Model Drift: Explore how Model Drift relates to Model Monitoring
Why It Matters
Understanding Model Monitoring is crucial for anyone working with ai infrastructure & deployment. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Data Drift
Changes in input data distribution over time that can degrade model performance in production.
MLOps
Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.
Model Drift
Degradation of model performance over time due to changes in the relationship between features and target.