Overview
Model monitoring in machine learning refers to the ongoing process of tracking, evaluating, and analyzing the performance of a machine learning model after it has been deployed into production. The goal is to ensure that the model continues to perform well over time and to detect any issues that may arise due to changes in the data or other factors.
Machine learning models often face challenges in production, such as:
- Data Drift: Changes in the underlying data distribution that affect the model's accuracy.
- Concept Drift: Changes in the relationship between input features and target variables over time.
- Model Degradation: A decline in model performance due to factors like outdated training data or changes in real-world behavior.
- Outlier/Anomaly Detection: Identifying unusual or unexpected data patterns that the model has not been trained on.
- Bias and Fairness: Monitoring to detect biases or fairness issues, ensuring the model does not unfairly treat different groups.
- Resource Utilization: Monitoring the model’s computational performance (e.g., latency, throughput, memory usage).
Effective model monitoring is essential for ensuring that models continue to provide accurate, reliable predictions over time, especially in dynamic environments where data and conditions change.