“Stop Worshipping Architectures”: Why Most Students Need Boring Data and Evaluation Skills Before the Next Fancy Model
A lot of ML students are building castles on sand and then acting surprised when the sand refuses to hold the castle. The castle is the shiny architecture. The sand is the dataset and the evaluation. The sand always wins. This post pushes one central idea: a student who can build clean datasets and reliable evaluations will outgrow the student who can recite five new architectures per week . The second student usually ships screenshots. The first ships systems that survive contact with reality. Read: Let Machine Learning Turn into Your Side Hustle with Automated Content Generation The architecture addiction (and why it sticks) Architecture worship is understandable. New models feel like progress you can point at. A new block diagram fits into a tweet. A new attention variant looks impressive in a college presentation. A leaderboard score can be waved around like a certificate, and nobody asks uncomfortable questions about where the dataset came from. Data work and evaluation wo...
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