Why Vision AI Models Fail

Prevent costly AI failures in production by mastering data-centric approaches to detect bias, classimbalance, and data leakage before deployment impacts your business.

• The four most common model failure modes that jeopardize production vision systems
• Real-world case studies from Tesla, Walmart, and TSMC showing how failures translate to business losses
• Data-centric failure modes including insufficient data, class imbalance, labeling errors, and bias
• Evaluation frameworks and quantitative methods for future-proofing your deployments
• Key strategies for detecting, analyzing, and preventing model failures including avoiding data leakage
• Production monitoring approaches to track data drift and model confidence over time