SageMaker, Amazon Web Services' powerful machine learning platform, offers a comprehensive suite of tools designed for data scientists to experiment, train datasets, and develop models.
However, it's important to note that while SageMaker provides robust capabilities for machine learning and data processing tasks, its services can become quite costly.
Especially for data science teams engaged in extensive experimentation and training, the expenses can accumulate rapidly.
Therefore, efficient management and optimization of SageMaker resources are crucial to balance its powerful features with cost-effective operations.
We view SageMaker Notebooks with low activity as unnecessary waste.
SageMaker Endpoints exhibiting low activity are considered unnecessary waste.
SageMaker Apps that demonstrate low activity are regarded as unnecessary waste.