Overview
Model monitoring is the process of tracking the performance of machine learning models after they are deployed. It helps ensure that models continue to make accurate predictions over time. By monitoring models, we can detect issues like data drift and performance degradation, allowing us to take corrective actions. Understanding model monitoring is essential for maintaining the reliability and effectiveness of machine learning applications.
📚 Key Learning Objectives
- ✓ Explain the importance of model monitoring in machine learning.
- ✓ Identify key metrics used in model performance evaluation.
- ✓ Demonstrate how to set up a monitoring system for a deployed model.
- ✓ Analyze model performance data to detect potential issues.
- ✓ Implement strategies for model retraining based on monitoring results.
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