Amazon launches SageMaker Canvas for no-code AI model development

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During its keynote address at its Re: Invent 2021 conference today, Amazon announced the 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 results.

Low- and no-code platforms allow developers and non-developers to create software through visual dashboards instead of traditional programming. 41% of organizations used low- or no-code tools in 2019/2020, up from 34% in 2018/2019, with the latest outsourcing report, the adoption process increasing.

“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 on stage. “Canvas uses terminology and visualization that is already familiar [users] And complements the data analysis tools that [people are] Already using. “

AI without code

With Canvas, Selipsky says customers can browse and access petabytes of data from 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 model is created, users can explain and interpret the model and share the model with each other to enrich collaboration and insights.

“With Canvas, we’re making it easier to prepare and collect data for machine learning in order to train models faster and expand machine learning to a wider audience,” added Selipsky. “It will really enable a whole new group of users to take advantage of their data and use machine learning to create new business insights.”

Canvas Data follows the wait for Sagemaker updates released earlier this year, including Wrangler, Feature Store and Pipelines. Data Wrangler recommends conversions based on data in the target dataset and applies these conversions to features. The feature store acts as a storage component for features and can access features in batches or subsets. Speaking of pipelines, it allows users to define, share, and reuse every step of the end-to-end machine learning workflow with pre-configured customized workflow templates while logging each step in Sagemaker experiments.

With over 82% of companies saying that custom application development outside of IT is important, Gartner predicts that by 2024, 65% of all applications will be built using low- and no-code platforms. Another study reports that 85% of engineering leaders out of 500 believe that law and no-code will become commonplace in their organizations by 2021.

If the current trend continues, the low and no-code market could grow to between $ 13.3 billion and $ 17.7 billion in 2021 and $ 58.8 billion and .4 125.4 billion in 2027.


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