Amazon launches SageMaker Canvas for no-code AI model development

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.

Low- and no-code platforms allow developers and non-developers alike to create software through visual dashboards instead of traditional programming. Adoption is on the rise, with a recent OutSystems report showing that 41% of organizations were using a low- or no-code tool in 2019/2020, up from 34% in 2018/2019.

“Now, business users and analysts can use Canvas to generate highly accurate predictions using an intuitive, easy-to-use interface,” AWS CEO Adam Selipsky said onstage. “Canvas uses terminology and visualizations already familiar to [users] and complements the data analysis tools that [people are] already using.”

AI without code

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, as well as 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 released earlier in the year, including Data Wrangler, Feature Store, and Pipelines. Data Wrangler 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. As for Pipelines, it 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.

With upwards of 82% of firms saying that custom app development outside of IT is important, Gartner predicts that 65% of all apps will be created using low- and no-code platforms like Canvas by 2024. Another study reports that 85% of 500 engineering leaders think that low- and no-code will be commonplace within their organizations as soon as 2021.

If the current trend holds, the market for low- and no-code could climb to between $13.3 billion and $17.7 billion in 2021 and between $58.8 billion and $125.4 billion in 2027.