Automated model tuning with SageMaker hyperparameter tuning
Hyperparameter tuning (HPT) helps you find the right parameters to use with your ML algorithm or the neural network to find an optimal version of the model. Amazon SageMaker supports managed hyperparameter tuning, also called automatic model tuning. In this section, we discuss the best practices to consider while configuring hyperparameter jobs on Amazon SageMaker.
To execute a SageMaker hyperparameter tuning job, you specify a set of hyperparameters, a range of values to explore for each hyperparameter, and an objective metric to measure the model's performance. Automatic tuning executes multiple training jobs on your training dataset with the ML algorithm and the hyperparameter values to find the best-performing model as measured by the objective metric.
In the following code blocks, we will see how to create an HPT job on SageMaker:
- First, initialize the hyperparameter names and range of values for each...
