Amazon SageMaker Canvas enables faster onboarding with automatic data import from local disk
Amazon SageMaker Canvas now enables faster onboarding allowing users to import data from their local disk automatically, without additional steps. SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code. SageMaker Canvas makes it easy to access and combine data from a variety of sources, automatically clean data, and build ML models to generate accurate predictions with a few clicks.
Amazon SageMaker Canvas now enables faster onboarding allowing users to import data from their local disk automatically, without additional steps. SageMaker Canvas is a visual point-and-click interface that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code. SageMaker Canvas makes it easy to access and combine data from a variety of sources, automatically clean data, and build ML models to generate accurate predictions with a few clicks.
SageMaker Canvas allows users to import data from a variety of sources including Amazon S3, Amazon Redshift, Snowflake, and local disk. Starting today, users can directly upload datasets from their local disk to SageMaker Canvas, without the need to contact their administrators as the required permissions are enabled by default. For administrators, the “Enable Canvas permissions” setting is enabled while setting up a domain, allowing SageMaker to attach a cross-origin resource sharing (CORS) policy to the default Amazon S3 bucket for uploading local files. If administrators don’t want domain users to upload local files automatically, they can choose to disable this setting.
Faster onboarding with automatic data onboarding from local disk is now available in all AWS regions where SageMaker Canvas is supported. To learn more and get started, please refer to the Amazon SageMaker Canvas documentation and the SageMaker Canvas product page.