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Optimizing Hyperparameter Tuning for Large ML Models

The author struggles with optimizing hyperparameters for large ML models that take a day to train. They need to balance hyperparameter tuning (HPO) with full training runs, ensuring that HPO-optimized parameters translate to full training runs. The author questions how to manage parameter drift between HPO and full training runs.

Computing & Technology, Computer Science, Machine Learning