Amazon SageMaker Canvas expands content summarization and information extraction capabilities
Amazon SageMaker Canvas is a service for business analysts to generate machine learning (ML) and artificial intelligence (AI) predictions without having to write a single line of code. As announced on October 5, customers can access and evaluate foundation models (FMs) to generate and summarize content.
Amazon SageMaker Canvas is a service for business analysts to generate machine learning (ML) and artificial intelligence (AI) predictions without having to write a single line of code. As announced on October 5, customers can access and evaluate foundation models (FMs) to generate and summarize content.
Starting today, Amazon SageMaker Canvas expands content summarization and information extraction capabilities of data specifically from customer-provided documents.
To get started, customers index a set of documents in Amazon Kendra that they would like to extract information from, and then simply select this document index for use in Amazon SageMaker Canvas. The new capabilities are available in all AWS regions where Amazon SageMaker Canvas, Amazon Kendra, Amazon Bedrock, and Amazon SageMaker JumpStart are supported today. To learn more, see the SageMaker Canvas service documentation. Customers are charged for their Amazon Kendra use, see the Amazon Kendra pricing for details. Customers using FMs from Amazon Bedrock are charged based on the volume of input tokens and output tokens. For more information, see Amazon Bedrock pricing page. Public FMs are deployed on Amazon SageMaker instances. Customers are charged for the duration of usage based on the instance type used. For more information, see the Amazon SageMaker Pricing for Hosting: Real-Time Inference.