<p>Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.</p> <p>A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-view-compare.html#experiments-view">View Experiments, Trials, and Trial Components</a>.</p> <important> <p>Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.</p> </important>

{
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    "id": "abc123",
    "created_at": "2025-01-01T00:00:00Z"
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}

{
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  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid request parameters"
  }
}