Right-sizing resources and avoiding unnecessary costs in Amazon SageMaker

Amazon SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design allows you to pick and choose the features that suit your use cases at different stages of the ML lifecycle. Amazon SageMaker 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 this post, we outline the pricing model for Amazon SageMaker and offer some best practices on how you can optimize your cost of using Amazon SageMaker resources to effectively and efficiently build, train, and deploy your ML models. In addition, the post offers programmatic approaches for automatically stopping or detecting idle resources that are incurring costs, allowing you to avoid unnecessary charges. Machine Learning is an iterative process with different computational needs for prototyping the code and exploring the dataset, processing, training, and hosting the model for real-time and offline predictions. In a traditional paradigm, estimating the right amount of computational resources to support different workloads is difficult, and often leads to over-provisioning resources. The modular design of Amazon SageMaker offers flexibility to optimize the scalability, performance, and costs for your ML workloads depending on each stage of the ML lifecycle. The following diagram is a simplified illustration of the modular design for each stage of the ML lifecycle.