'Model Tests are Essential for Building Domain Knowledge' machinelearning mlops
Testing protects against regressions. But the most important product of testing is the domain knowledge and powerful capabilities.
Testing often conjures a feeling of a chore, something that is good practice but not always necessary. Classic questions around testing arise, such as what is necessary to test and how sophisticated these tests should be. But viewing tests as building and systematizing domain knowledge shows the power of testing beyond a ratchet to prevent backsliding.
Finding out about these strange cases is learned through experience, or tacit knowledge. In order to leverage tacit knowledge, it needs to be encoded into systems. Now imagine a situation where “pedestrians” as a model’s target label may be suffering in performance. Pedestrians with costumes along with pedestrians with canes or walkers, scooter-riders, and other moving obstructions might be valuable concepts to use in a query, perhaps to understand why a pedestrian-detection model may be failing, or to surface unlabeled examples for further labeling.
These interfaces make it easy for a developer to add a test, decreasing the likelihood of model regressions and building the foundations for knowledge sharing.Andrej Karpathy describes workflows and capabilities atop the internal platform enabling autonomous vehicle development, which heavily relies on seeing labeling, testing, and error analysis in the same view.. As the product and labeling teams iterate, they improve on their tests and create new ones.
Developing these queries requires explicitly encoded domain knowledge. Some triggers require understanding concepts in autonomous vehicle development, like the difference between radar and vision or the definition of a bounding box. Other triggers leverage real-world concepts, like brake lights, roads, and what constitutes the roof of a vehicle. Many rely on both types of concepts together to implement a trigger.
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