Artificial Intelligence (AI) has transformed various industries, from personalized content recommendations to medical diagnostics. However, the development and deployment of robust AI models requires more than just training powerful algorithms on large datasets. Ensuring these models perform effectively in the real world hinges on two critical processes—evaluation and validation. These steps determine a model's reliability, accuracy, and ability to generalize.
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