At Petabyte Scale, ML Stops Being About Models
SMRTR summary
At petabyte scale, machine learning success depends less on model quality and more on data infrastructure — how data is stored, validated, and served. Companies like Meta, Google, and Netflix have learned that bottlenecks shift to data layout, feature correctness, and pipeline reliability, meaning only a small fraction of real-world ML systems is actually ML code.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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