AWS intros SageMaker Canvas no-code machine learning service
The service enables users to build machine learning models and generate accurate predictions without writing code. It supports both on-premises and cloud deployments.
AWS now has a no-code tool that enables enterprises to predict business outcomes based on data without needing machine learning expertise.
The tech giant introduced SageMaker Canvas at its AWS re:Invent conference on Nov. 30. The tool -- an addition to the SageMaker suite of AI services -- automatically cleans and combines an organization's data and can create hundreds of models behind the scenes, select the best performing one, and generate new individual or batch predictions, according to AWS.
SageMaker Canvas uses a visual point-and-click interface, enabling users to make predictions without writing code.
What SageMaker Canvas offers
"Canvas uses terminology and visualization that are already familiar to analysts and complements the data analysis tools they're already using," said Adam Selipsky, AWS CEO, during his keynote presentation.
With SageMaker Canvas, users can browse and access petabytes of data from both cloud and on-premises data sources such as Amazon S3, Redshift and local files. Once Canvas creates predictive models, users can publish the results, plan and interpret models to share dashboards, and collaborate with other data analysts.
Adam Selipsky introduces SageMaker Canvas, a new no-code tool for predictive data analysis during his keynote presentation at AWS re:Invent.
The problem SageMaker Canvas addresses
The idea is to automate the application development process, said Sid Nag, an analyst at Gartner.
"With the Canvas offering, [AWS is] extending the SageMaker functionality to make the life of the developer easier," Nag said.
He added that AWS provides users with tools that enable them to use a visual process to generate the code for machine learning applications that otherwise would need to be written by a developer or data scientist.
With the Canvas offering, [AWS is] extending the SageMaker functionality to make the life of the developer easier.
Sid NagAnalyst, Gartner
Since machine learning applications can be detailed and complex, Nag said, the SageMaker Canvas system will help simplify it for users who want to develop applications with AI.
"Anything that simplifies the generation of application[s] that leverage machine learning is a good thing," he said.
Nag added the technology offers additional options to enterprises already writing machine learning applications on AWS, and drives more adoption and application development on AWS.
As to the possible challenges enterprises using the new tool might face or how it compares to other no-code products, Nag said users will have to wait and see.
According to AWS, SageMaker Canvas supports multiple problem types, including binary and multiclass classification, numerical regression and time-series forecasting, so users can create applications to perform tasks such as fraud detection and inventory optimization.
Canvas is AWS's latest capability in the SageMaker line, which also includes SageMaker Studio, SageMaker Experiments and SageMaker Debugger among others.
SageMaker Canvas is now generally available in the U.S. and Europe.
