Automatic Model Tuning with Amazon SageMaker is now Generally Available. With this feature, Amazon SageMaker can automatically tune your model by adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions the model is capable of producing.

When you’re tuning your model to be more accurate, you have two big levers to pull, modifying the data inputs you provide the model (for example, taking the log of a number), and adjusting the parameters of the algorithm. These are called hyperparameters and finding the right values can be tough. Typically, you’ll start with something random and iterate through adjustments as you begin to see what effect the changes have. It can be a long cycle depending on how many hyperparameters your model has.

Amazon SageMaker simplifies this by offering automatic model tuning as an option during training. Amazon SageMaker will actually use machine learning to tune your machine learning model. It works by learning what affects different types of data have on a model and applying that knowledge across many copies of the model to quickly seek out the best possible outcome. As a developer or data scientist, this means you only really need to be concerned with the adjustments you want to make to the data you feed the model, which greatly reduces the number of things to worry about during training. When initiating automatic model tuning, you simply specify the number of training jobs through the API and Amazon SageMaker handles the rest.

Automatic Model Tuning is now available in the US East (N.Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), and Asia Pacific (Tokyo) AWS regions. Visit the documentation page for more information on Automatic Model Tuning and read the blog post for how to use Automatic Model Tuning on your training jobs.