Imagine a scenario where an AI model that performs exceptionally well in laboratory settings fails to deliver similar results in real-world applications. What could be the potential reasons and how might these be addressed?
- Data distribution mismatch between lab and real-world.
- Inadequate training data.
- Lack of computational power.
- The model is overfitting to the lab data.
A common reason for a well-performing AI model in the lab to fail in real-world applications is a data distribution mismatch. Addressing this issue involves collecting and using real-world data to better align the model with the target application's conditions.
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