# BDA7.2 Scalable Model Evaluation

This skill focuses on evaluating AI and ML models at scale in HPC environments. It covers techniques for benchmarking accuracy, performance, and resource efficiency across large datasets and distributed systems.

## Requirements

* External: Understanding of evaluation metrics and HPC resource usage
* Internal: BDA7.1 Cross-Validation (recommended)

## Learning Outcomes

* Design scalable evaluation pipelines for large datasets and models.
* Benchmark model performance across multiple hardware configurations or datasets.
* Monitor compute, memory, and I/O usage during model evaluation.
* Compare models using normalized, reproducible metrics and visualizations.
* Ensure fair evaluation through consistent preprocessing, baselining, and version control.


** Caution: All text is AI generated **