Millised on lähenemised ML mudelite testimiseks CI-s?

In CI, various approaches are applied for testing ML models, aimed at controlling data quality and model stability:

• Unit tests for data processing and model components — verification of the correctness of preprocessing, training, and prediction functions.
• Model quality testing on control datasets — automatic evaluation of metrics (accuracy, F1, AUC, etc.) whenever code or data changes.
• Monitoring of metrics and data drift — ensuring that the distribution of input data and model outputs has not changed significantly.
• Reproducibility tests of training — verifying that the model trains identically with the same data and parameters.
• Integration tests — checking the interaction of the model with other system components.

Automating these tests in CI helps quickly identify regressions and issues with data or model quality.