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This Whitepaper provides AI practitioners and data scientists with critical insights into common failure modes in computer vision systems, enabling you to build more reliable and production-ready AI models.
What you will learn about:
- 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
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IEEE Spectrum and Wiley are proud to bring you this white paper, sponsored by Voxel51.
More Information
When computer vision systems fail, the consequences are far-reaching. From autonomous vehicles misidentifying pedestrians to retail systems falsely flagging customers, the cost of AI model failure is high. This comprehensive guide examines why even the most advanced vision models often fail due to poor data quality, underrepresented edge cases, and model bias. Building robust, trustworthy AI systems requires more than architectural improvements—it demands a strong foundation in data curation, model evaluation, and analysis to prevent failures before they reach production.
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