Compare the main drift detection methods using these criteria:

• Type;
• Data compatibility;
• Interpretability;
• Practical considerations.

Population Stability Index (PSI):

• Type: statistical metric;
• Data compatibility: categorical and continuous data (after binning);
• Interpretability: high (single summary value);
• Practical considerations: easy to implement and explain; binning required for continuous features.

Kolmogorov–Smirnov (KS) Test:

• Type: non-parametric statistical test;
• Data compatibility: continuous, unbinned data;
• Interpretability: medium (outputs a statistic and p-value);
• Practical considerations: clear statistical basis, but less intuitive for non-technical users.

Model-based methods:

• Type: predictive modeling (e.g., classifiers);
• Data compatibility: any data type, including high-dimensional and mixed data;
• Interpretability: varies, often lower (depends on model used);
• Practical considerations: flexible and powerful for complex data, but can be resource-intensive and less transparent.

Follow these best practices for implementing drift monitoring in production:

• Choose the right method: Match the drift detection method to your data and business needs. Use PSI for tabular data with clear binning, KS for continuous features, and model-based methods for complex or high-dimensional data;
• Automate drift metrics: Set up automated calculation and reporting to ensure timely detection;
• Integrate alerting: Connect monitoring to alerting systems so significant drift triggers immediate notifications;
• Visualize results: Use dashboards and visualization tools to make drift metrics accessible to all stakeholders.

Explore open-source tools for production drift monitoring:

• Evidently: Provides dashboards and metrics for both statistical and model-based drift detection;
• WhyLabs: Delivers scalable, cloud-based monitoring with broad data and ML platform integrations.
• Both tools support continuous monitoring, historical analysis, and customizable alerts to help maintain model performance.