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 or performance degradation, allowing us to take action and improve our models as needed. Understanding model monitoring is crucial for maintaining the reliability of AI systems in real-world applications.
📚 Key Learning Objectives
- ✓ Define model monitoring and its importance.
- ✓ Identify key metrics for monitoring model performance.
- ✓ Explain data drift and its impact on models.
- ✓ Describe methods to detect performance degradation.
- ✓ Implement basic monitoring techniques for machine learning models.
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