Amazon SageMaker AI Canvas

Amazon SageMaker AI Canvas is a feature of Amazon SageMaker AI that provides a no-code solution for image classification. Without writing a line of code, you can start classifying images by label, or you can create a labeled image set, train a classifier, and deploy an endpoint.

In SageMaker AI Canvas, you can use ready-to-use foundation models (FMs), or you can build your own custom ML model. The ready-to-use models can extract insights from your data for a variety of use cases. Ready-to-use models are powered by Amazon AI services, including Amazon Rekognition, Amazon Textract, and Amazon Comprehend. You only have to import your data and start using a solution to generate predictions. If you want a model that is customized to your use case and trained with your data, you can build a model.

Unlike Amazon Rekognition Custom Labels, you can control the deployment compute instance. This helps you control costs more precisely. If you are processing a few thousand images a month or more, SageMaker AI can be more cost effective than Amazon Rekognition.

The following are the advantages of SageMaker AI Canvas:

• Data labeling and processing pipeline in one place

• Automated training

• Ability to select the instance type for your endpoint deployments

The following are the disadvantages of SageMaker AI Canvas:

• Currently supports only single-label classification

• No control over objective function, network architecture, or initial model weights

For more information, see the following:

• Getting started with using Amazon SageMaker AI Canvas in the Amazon SageMaker AI Developer Guide.

• Use ready-to-use models in the Amazon SageMaker AI Developer Guide.

• Use custom models in the Amazon SageMaker AI Developer Guide.