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Update AITG-MOD-06_Testing_for_Robustness_to_New_Data.md

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Matteo Meucci
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@@ -28,7 +28,7 @@ This test identifies vulnerabilities associated with the lack of robustness to n
- **Periodically Retrain the Model**: Schedule regular retraining of the model on fresh data that includes recent production data. This ensures the model stays up-to-date with the latest data distributions.
- **Domain Adaptation Techniques**: If you anticipate specific types of drift, use domain adaptation techniques during training to explicitly teach the model to be invariant to those changes.
### Suggested Tools for this Specific Test
### Suggested Tools
- **DeepChecks**: An open-source Python library for validating and testing ML models and data, with a strong focus on detecting data drift, corruption, and other issues.
- Tool Link: [DeepChecks on GitHub](https://github.com/deepchecks/deepchecks)
- **Evidently AI**: An open-source Python library for evaluating, testing, and monitoring ML models in production. It provides interactive reports on data drift and model performance.