Model tuning

In Chapter 7, Evaluating and Optimizing Models, you learned many important concepts about model tuning. Let’s now explore this topic from a practical perspective.

In order to tune a model on SageMaker, you have to call create_hyper_parameter_tuning_job and pass the following main parameters:

• HyperParameterTuningJobName: This is the name of the tuning job. It is useful to track the training jobs that have been started on behalf of your tuning job.
• HyperParameterTuningJobConfig: Here, you can configure your tuning options. For example, which parameters you want to tune, the range of values for them, the type of optimization (such as random search or Bayesian search), the maximum number of training jobs you want to spin up, and more.
• TrainingJobDefinition: Here, you can configure your training job. For example, the data channels, the output location, the resource configurations, the evaluation metrics, and the stop conditions.

In SageMaker...