A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run…

Setting the `Strategy` to `Bayesian` optimizes the selection of hyperparameter combinations across trials, making the overall tuning process more efficient, but it does not directly stop individual training jobs from running too long if they are not improving against the objective metric.

`RetryStrategy` configures the number of retries for failed training jobs within a hyperparameter tuning job and does not affect the duration of non-improving jobs.

Narrowing the `ParameterRanges` reduces the search space for hyperparameters, which can speed up the overall tuning process by exploring fewer combinations, but it does not specifically stop individual training jobs that are no longer improving against their objective metric.

Setting `TrainingJobEarlyStoppingType` to `AUTO` in SageMaker automatic model tuning enables SageMaker to automatically detect when a training job is unlikely to improve further based on the objective metric and stop it early. This directly addresses the problem of tuning jobs taking too long and continuing without significant improvement, thereby speeding up the hyperparameter optimization process.