Techniques for detecting and correcting data drift

In machine learning, it's essential to understand how to maintain a deployed model's performance over time. One familiar challenge practitioners face is data drift, which occurs when the distribution of input data changes. Data drift can lead to decreased model performance and inaccurate predictions.

What are the appropriate steps for detecting and correcting data drift in machine learning models? (Select all that apply).