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.

• 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.

• BDA7.1 Cross-Validation
• BDA7.2 Scalable Model Evaluation

Caution: All text is AI generated