This happens because production data rarely behaves the same way as training data.

In most real systems, training data is curated and static, while live data reflects changing user behavior, incomplete inputs, or upstream changes. Even small shifts in feature distributions can significantly affect predictions if the model was never exposed to them.

Start by comparing feature distributions between training and production data. Track statistics like means, ranges, null counts, and category frequencies. If you use preprocessing steps such as scaling or encoding, ensure they are applied using the exact same logic and artifacts during inference.

In some cases, the issue is training–serving skew caused by duplicating preprocessing logic in different places. Centralizing feature transformations helps avoid this.

Common mistakes include:

• Retraining models without updating preprocessing artifacts

• Assuming validation data represents real-world usage

• Ignoring missing or malformed inputs in production

The practical takeaway is to monitor input data continuously and treat data quality as a first-class production concern.