Why You Should Break Your ML Pipelines on Purpose

Machine learning systems fail differently than traditional software, often degrading silently while continuing to serve increasingly inaccurate predictions. Unlike infrastructure failures that crash loudly, AI pipelines can quietly break due to stale data, feature drift, or model version mismatches, causing recommendation engines to suggest irrelevant items or chatbots to provide incoherent responses. Chaos engineering, which intentionally injects faults to test system resilience, helps teams proactively identify weaknesses in ML pipelines before they cause real damage.