Save costs by automatically shutting down idle resources within Amazon SageMaker Studio

Amazon SageMaker Studio provides a unified, web-based visual interface where you can perform all machine learning (ML) development steps, making data science teams up to 10 times more productive. Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. Studio notebooks are collaborative notebooks that you can launch quickly because you don't need to set up compute instances and file storage beforehand. Amazon SageMaker is a fully managed service that offers capabilities that abstract the heavy lifting of infrastructure management and provides the agility and scalability you desire for large-scale ML activities with different features and a pay-as-you-use pricing model. In Studio, running notebooks are containerized separately from the JupyterServer UI in order to de-couple compute infrastructure sizing.