AI Evaluation vs Ad Hoc Testing
Developers should learn AI Evaluation to build trustworthy and reliable AI systems, especially in high-stakes domains like healthcare, finance, or autonomous vehicles where errors can have severe consequences meets developers should use ad hoc testing during early development phases, after bug fixes, or when rapid feedback is needed, as it helps uncover unexpected issues and usability problems. Here's our take.
AI Evaluation
Developers should learn AI Evaluation to build trustworthy and reliable AI systems, especially in high-stakes domains like healthcare, finance, or autonomous vehicles where errors can have severe consequences
AI Evaluation
Nice PickDevelopers should learn AI Evaluation to build trustworthy and reliable AI systems, especially in high-stakes domains like healthcare, finance, or autonomous vehicles where errors can have severe consequences
Pros
- +It is essential for model validation, regulatory compliance, and iterative improvement, helping teams identify issues like overfitting, data drift, or unfair outcomes before deployment
- +Related to: machine-learning, data-science
Cons
- -Specific tradeoffs depend on your use case
Ad Hoc Testing
Developers should use ad hoc testing during early development phases, after bug fixes, or when rapid feedback is needed, as it helps uncover unexpected issues and usability problems
Pros
- +It's particularly valuable for exploratory testing to understand application behavior, complementing formal testing methods like unit or integration tests
- +Related to: exploratory-testing, manual-testing
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use AI Evaluation if: You want it is essential for model validation, regulatory compliance, and iterative improvement, helping teams identify issues like overfitting, data drift, or unfair outcomes before deployment and can live with specific tradeoffs depend on your use case.
Use Ad Hoc Testing if: You prioritize it's particularly valuable for exploratory testing to understand application behavior, complementing formal testing methods like unit or integration tests over what AI Evaluation offers.
Developers should learn AI Evaluation to build trustworthy and reliable AI systems, especially in high-stakes domains like healthcare, finance, or autonomous vehicles where errors can have severe consequences
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