In many production ML systems, models perform well during training and validation but degrade significantly once deployed. One common reason is data leakage, where information from the target variable or future data unintentionally enters the training process.

For example, leakage can occur through:

• Improper feature engineering

• Data preprocessing performed before train/test split

• Time-series leakage

• Target-derived features

In practice, detecting leakage is not always straightforward, especially in complex pipelines involving feature stores, automated preprocessing, and multiple data sources.

What techniques or validation strategies do you use to identify and prevent data leakage in real-world ML workflows?
Are there specific tools, pipeline structures, or testing approaches that help ensure models remain robust after deployment?