Edge Case
What is an edge case?
An edge case is an uncommon or unusual scenario that falls outside the typical patterns but still needs to be handled by your product. In the context of AI and data, edge cases represent the less common inputs or situations—like mice and zebras when classifying cats and dogs—that your dataset or system needs to account for to ensure comprehensive coverage and reliability.
These scenarios can be difficult to anticipate without real data or thorough testing.
Why do edge cases matter for AI products?
When building datasets to evaluate AI products, you need to include edge cases alongside your main categories. If you're building a classifier for cats and dogs, your dataset shouldn't just have lots of cats and dogs. It should also include mice, horses, zebras, and other animals your system might encounter.
Without edge cases in your dataset, you can't know how your AI will behave when it encounters unusual inputs in production. These uncommon scenarios often reveal weaknesses in your system that don't show up when testing only typical examples.
How do teams identify and handle edge cases?
Edge cases become easier to track down when you have good observability into user behavior. When problems occur, you can pull up behavior profiles to see what events led up to the issue, helping you understand the specific circumstances that triggered the edge case.
For AI products, unit tests help ensure you're covering edge cases systematically. Testing tools can generate tests for various error scenarios—including edge cases—that you might not have the patience to write manually. This comprehensive test coverage is essential for building reliable AI products.
Learn more:
- The Ethics of the Data We Collect
- AI Evals & Discovery - All Things Product Podcast with Teresa Torres & Petra Wille
- 21 Ways to Use AI at Work (And Build Your AI Product Toolbox)
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