Drift Detection in Categorical Features is a specialized solution designed to identify and monitor changes in categorical data distributions within machine learning (ML models. By detecting shifts in categorical feature distributions, this tool ensures that ML models maintain their accuracy and reliability over time. Key Features and Functionality: - Categorical Data Monitoring: Continuously observes categorical features to detect deviations from established baselines. - Statistical Analysis: Utilizes statistical tests, such as the Chi-square test, to assess the significance of detected drifts. - Automated Alerts: Generates notifications when significant drift is identified, enabling prompt intervention. - Integration with AWS Services: Seamlessly integrates with Amazon SageMaker Model Monitor for comprehensive model oversight. Primary Value and Problem Solved: This solution addresses the challenge of data drift in categorical features, which can lead to degraded model performance if left unchecked. By providing real-time detection and alerting of categorical data shifts, it empowers data scientists and ML engineers to proactively retrain models, ensuring sustained accuracy and effectiveness in dynamic data environments.