Benchmark accuracy is one of the most trusted signals in machine learning. It tells you which model performs best—and it often drives decisions about what gets deployed. But what if that number is giving you a false sense of confidence? 

In this episode, we break down why models that perform well on benchmarks often fail in real-world settings. You will learn how dataset assumptions, evaluation metrics, and deployment conditions create a gap between leaderboard success and practical reliability. 

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