5 documented AWS SageMaker cost inefficiencies, each with how to detect it and how to fix it.
AWS SageMakerCER-0333
SageMaker notebook instances are billed continuously while in an active state - and critically, they do not automatically shut down when idle. Closing a browser tab, shutting down a Jupyter kernel, or simply walking away does not stop the...
AWS SageMakerCER-0383
In SageMaker Studio, each running JupyterLab or Code Editor application runs on an instance that is billed for as long as the application is running, whether or not the user is doing anything. Data scientists routinely leave spaces running...
AWS SageMakerCER-0384
SageMaker training and hyperparameter tuning jobs run on on-demand instances unless managed spot training is explicitly enabled on the job. Many training workloads tolerate interruption: experiments, scheduled retraining, tuning sweeps and...
AWS SageMakerCER-0559
A SageMaker real-time inference endpoint keeps its instances running and billed for as long as the endpoint is in service, whether or not any application calls it. Endpoints are created for model evaluations, demos, A/B tests, proofs of...
AWS SageMakerCER-0563
Many SageMaker AI environments have a predictable instance baseline: real-time inference endpoints that serve production traffic around the clock, recurring training and processing jobs, batch transform runs and notebooks used every...
