A bank used SageMaker with the built-in XGBoost algorithm and ran a Bayesian automatic hyperparameter tuning job using validation accuracy as the objective. The deployed model currently has 75% accuracy. The bank has given the ML specialist one day to improve production accuracy. What is the fastest way to try to improve the model within that time?

A warm start hyperparameter tuning job reuses information from previous tuning jobs, allowing it to converge on better hyperparameters more quickly. Since the goal is to improve accuracy within one day, this approach is the most efficient. Changing the objective metric (AUC or F1 score) might be beneficial in the long run but would require the tuning job to start from scratch, consuming more time. Incremental training is for continuing training a model on new data, not for hyperparameter optimization. Doubling the number of training jobs without leveraging previous results would also take longer.