A company training a deep learning model on SageMaker wants to optimize hyperparameters to minimize validation loss using the least compute time. Which tuning strategy should they choose?

Hyperband is the most suitable strategy because it efficiently prunes poorly performing hyperparameter configurations early in the training process, saving significant compute time. It does this by allocating more resources (e.g., epochs) to promising configurations while quickly discarding less effective ones. Grid search and random search are less efficient as they don't adaptively allocate resources; grid search exhaustively checks predefined points, and random search samples randomly, both often wasting compute on unpromising configurations. Bayesian optimization is generally more efficient than grid or random search but can be slower than Hyperband in scenarios where many configurations need to be evaluated, as it builds a probabilistic model of the objective function which adds overhead.