Amazon SageMaker Feature Store adds operational metrics to easily monitor feature groups
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference. Until today, SageMaker Feature Store monitoring was limited to consumed read and write units, which gave a limited view of the operational efficiency of the feature store.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference. Until today, SageMaker Feature Store monitoring was limited to consumed read and write units, which gave a limited view of the operational efficiency of the feature store.
Today, Amazon SageMaker Feature Store is announcing the availability of new monitoring metrics logged to Amazon CloudWatch, including number of API requests, errors, throttled requests, and latency of the service in processing operations. In addition, you can now track storage size for the online store over time. These metrics gives you a unified view of SageMaker Feature Store operational health to help troubleshoot issues, discover insights and keep your applications running smoothly. With Amazon CloudWatch, you can use the new metrics to create customized dashboards and set alarms that notify you or take actions when a specified metric reaches a threshold.
To learn more, please view the documentation here. To get started, log into the Amazon SageMaker console.