Learn Tracking Model Drift and Concept Drift by understanding how machine learning models change in performance over time due to variations in data and real-world behavior. This training explains data drift, where input data distributions change, and concept drift, where the relationship between inputs and outputs shifts. It also covers how to monitor model accuracy, detect performance degradation, and use statistical methods to identify drift early. You will learn how to set up monitoring systems, compare historical and live data, and take corrective actions such as retraining models. The course focuses on maintaining model reliability, stability, and long-term performance in production environments.
Showing the single result
