# BDA7 Evaluation and Benchmarking

This node introduces the principles and tools for evaluating machine learning models in large-scale HPC environments. It covers statistical evaluation methods, reproducibility techniques, and scalable benchmarking strategies for AI workloads.

## Learning Outcomes

* Apply statistical evaluation techniques to assess the generalization and reliability of ML models.
* Use cross-validation and related methods to quantify model performance.
* Benchmark AI models at scale with a focus on consistency, fairness, and comparability.

## Subskills

* [[skill-tree:bda:7:1:b]]
* [[skill-tree:bda:7:2:b]]

** Caution: All text is AI generated **