Hyperparameter Tuning
Domain: MLOps
🎯 Motivation
Finding the best hyperparameters (learning rate, batch size, model size) through systematic search.
📋 Concrete Example
Tuning: try lr=0.001 (88%), lr=0.01 (85%), lr=0.0001 (82%). Best: lr=0.001. Systematic search finds optimal settings.
🔗 Analogy
Tuning a guitar - try different string tensions until it sounds right. Hyperparameter tuning tries different settings until the model performs best.