Amazon SageMaker has announced the support of three new completion criteria for Amazon SageMaker automatic model tuning, providing you with an additional set of levers to control the stopping criteria of the tuning job when finding the best hyperparameter configuration for your model.
In this post, we discuss these new completion criteria, when to use them, and some of the benefits they bring.
SageMaker automatic model tuning
Automatic model tuning, also called hyperparameter tuning, finds the best version of a model as measured by the metric we choose. It spins up many training jobs on the dataset provided, using the algorithm chosen and hyperparameters ranges specified. Each training job can be completed early when the objective metric isn’t improving significantly, which is known as early stopping.
Until now, there were limited ways to control the overall tuning job, such as specifying the maximum number of training jobs. However, the selection of this

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