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During a keynote address today at its re:Invent 2021 conference, Amazon announced SageMaker Canvas, which enables users to create machine learning models without having to write any code. Using SageMaker Canvas, Amazon Web Services (AWS) customers can run a machine learning workflow with a point-and-click user interface to generate predictions and publish the results.
“Now, business users and analysts can use Canvas to generate highly accurate predictions using an intuitive, easy-to-use interface,” AWS Adam Selipsky said onstage. “Canvas uses terminology and visualizations already familiar to [users] and complements the data analysis tools that [people are] already using.”
With Canvas, Selipsky says that customers can browse and access petabytes of data from both cloud and on-premises data sources, such as Amazon S3, Redshift databases, and local files. Canvas uses automated machine learning technology to create models, and once the models are created, users can explain and interpret the models and share the models with each other to collaborate and enrich insights.
“With Canvas, we’re making it even easier to prepare and gather data for machine learning to train models faster and expand machine learning to an even broader audience,” Selipsky added. “It’s really going to enable a whole new group of users to leverage their data and to use machine learning to create new business insights.”
Canvas follows on the heels of SageMaker improvements earlier in the year, including Data Wrangler, Feature Store, and Pipelines. Data Wrangle recommends transformations based on data in a target dataset and applies these transformations to features. Feature Store acts as a storage component for features and can access features in either batches or subsets. And Pipelines allows users to define, share, and reuse each step of an end-to-end machine learning workflow with preconfigured customizable workflow templates while logging each step in SageMaker Experiments.
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