Whitepaper
Why Vision AI Models Fail
Why do vision AI models break down in the real world—even when they look perfect on paper? To find out, we analyzed the most common failure patterns across high-stakes domains like autonomous driving, retail, and healthcare. This guide outlines why most breakdowns are data failures in disguise—and what it really takes to build robust, production-ready models.
Get practical takeaways and field-tested strategies, including:
- Data failure modes: How label noise, imbalance, and bias quietly derail model accuracy
- Real-world incidents: What Amazon, Tesla, and TSMC got wrong—and what it cost them
- Debugging the invisible: Techniques for spotting silent failures standard metrics miss
- Visual QA workflows: Tools to find mislabeled, biased, or low-quality samples—before deployment
- Fix-before-fail strategy: How leading teams use data-centric practices to prevent outages and regressions