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Evaluation Metrics and Data Splits

Fast predictions can still be wrong. Calculate precision and recall from explicit outcomes, choose thresholds on held-out data, and keep quality evidence separate from serving latency and availability.

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RELATED CONCEPTS

Serving Benchmarks That Do Not Lie Evaluation and Data Pipeline Infrastructure SLOs for AI Systems Pretraining, Fine-Tuning and Distillation

LESSONS THAT TEACH THIS

From Model to ServiceMeasure useful answers before renting more compute14 min→From Model to ServiceCapstone: defend a measured model improvement45 min→

PRACTICE THIS IN REAL QUESTIONS

GPU & Accelerator ArchitectureWhat is a tensor core, and what does a kernel have to do to actually use one?→Open-Weights Models & Serving EnginesThe model ships in FP8. Should you requantize to four bits to fit more of it on fewer GPUs?→Hardware, Cabling & Cluster Build-OutA vendor claims their accelerator beats an H100 at half the price. How do you evaluate that?→Open-Weights Models & Serving EnginesThree open-weights models could serve your product. How do you choose?→Open-Weights Models & Serving EnginesYou need a released bf16 model at half the footprint. Walk through quantizing it yourself.→Open-Weights Models & Serving EnginesDesign the evaluation you run against a serving deployment, not against a model.→