How and Why to Use Custom SageMaker Images If you have used SageMaker for data science modeling work, you have likely used the AWS-provided images to train your models and possibly deploy them to an endpoint. These provide images for scikit-learn, xgboost and deep-learning among other common frameworks. This article will address an issue we had with the provided images […]
Introduction S3 Lifecycles provide a fantastic way to manage the lifetimes of S3 Objects. Working in AWS and in bigger organizations, you will inevitably have objects lingering (often in standard storage), costing you money. Lifecycle rules offer an easy way to trim some fat, which can both help you save money and exist in a tidier […]
First things first, what is data quality monitoring? Data quality monitoring for machine learning can generally be thought of from two perspectives. One perspective is that of traditional data-engineering. This type of monitoring is concerned with the “physical” characteristics of the data and ensuring they are what you expect them to be. It involves criteria […]
Disclaimer about Changes to Sagemaker Studio As of Nov. 30 2023, there have been major changes to Sagemaker Studio. Existing customers of Sagemaker Studio will get the default experience now called Sagemaker Studio Classic — this is the Studio experience this article was written for. New Sagemaker Studio customers (and existing customers that choose to […]