AWS today announced a new machine learning service, Amazon SageMaker Canvas. Unlike its existing machine learning services, the target audience here isn’t highly technical data scientists and engineers but any engineer or business user inside a company. The promise of SageMaker Canvas is that it will allow anybody to build machine learning prediction models, using a point-and-click interface.
If that sounds familiar, it may be because Azure and others offer similar tools, though AWS may have the advantage that a lot of companies already store all of their data in AWS anyway.
“SageMaker Canvas leverages the same technology as Amazon SageMaker to automatically clean and combine your data, create hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions,” writes AWS’ Alex Casalboni in today’s announcement. “It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types let you address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.”
Unsurprisingly, the service is backed by SageMaker, AWS’s fully managed machine learning service.
The general idea here is that users can use any dataset, down to a basic CSV file, and then decide which of the columns in this dataset Canvas should predict. There’s no need to worry about how to train this model. Yet while this is a far easier user experience that using traditional ML tools, we’re still not quite talking drag-and-drop either. This is AWS, after all. The overall experience is more akin to working in the AWS console than a modern no-code application.