Silent failures usually indicate logical or data issues rather than system errors.

Most prediction services return outputs even when inputs are invalid, poorly scaled, or missing key signals. Without input validation or prediction sanity checks, these failures remain invisible.

Begin by logging raw inputs and model outputs for a small sample of requests. Compare them against expected ranges from training data. Add lightweight validation rules to detect out-of-range values or missing fields before inference.

If your model relies on feature ordering or strict schemas, verify that request payloads still match the expected format. Even a reordered column can produce incorrect results without triggering errors.

Common mistakes include:

• Disabling logs for performance reasons

• Trusting upstream systems blindly

• Assuming the model will fail loudly when inputs are wrong

A good takeaway is to design inference systems that fail safely and visibly, even when predictions technically succeed.