Summary

In this chapter, we explored the concept of drift, which affects the performance of deployed deep learning models over time. We covered the three types of drift – concept drift, data drift, and model drift – and discussed strategies to handle them effectively. This included strategies to approach drift, including automatic programmatic detection and manual domain expert predictions, strategies to quantify drift, and strategies to mitigate drift effectively. We learned that statistical-based drift should always be opted for over ambiguous data distribution drift. We also learned that monitoring drift by batch in regular intervals is crucial in ensuring the continued success of deep learning models. Finally, using the evidently library, we demonstrated how to implement programmatic data distribution drift detection in a practical tutorial and understood behaviors that can shape how you think of data distribution drift methods. This knowledge can be applied across...