AWS Unveils SageMaker Canvas For ML Without Code

Leading public cloud provider Amazon Web Services (AWS) last week unveiled new ML capabilities that it says will make ML more accessible and cost-effective than ever. Of note is Amazon SageMaker Canvas, which AWS says was created to allow organizations to make accurate ML predictions without machine learning experience or having to write a single line of code.

As businesses increasingly turn to AI to reinvent their businesses and customer experiences, the perennial challenge revolves around the lack of specialized skills required to work with ML models. As this often requires years of formal education or intensive training with a challenging curriculum, the shortage of data scientists is unlikely to be resolved soon.

Overcoming lack of ML skills

According to AWS, Amazon SageMaker Canvas addresses the lack of specialized machine learning skills with a visual, point-and-click user interface that makes it easy for business analysts to generate predictions and publish results.

To start, customers simply point Amazon SageMaker Canvas to their data stores, which might be on-premises databases, locally stored files, or cloud-based locations such as Snowflake or Amazon Redshift.

Amazon SageMaker Canvas will then provide the visual tools to help users browse petabytes of data and will intuitively prepare and analyze data. Automated machine learning capabilities can then be leveraged to build and train machine learning models without any coding.

Models from Amazon SageMaker Canvas can also be exported to AWS’s own Amazon SageMaker Studio, an integrated development environment for ML. This allows models to be shared with data scientists to validate and further refine the models.

“Business users and analysts can use Canvas to generate highly accurate predictions using an intuitive, easy-to-use interface,” said AWS CEO Adam Selipsky at the keynote held in Las Vegas. “Canvas uses terminology and visualizations already familiar… and complements the data analysis tools that [users are] already using.”

“We’re excited to expand our industry-leading machine learning service to an even broader group of customers, so they too can drive innovation in their business and help solve challenging problems,” said Bratin Saha, vice president of machine learning at AWS.

“With these new Amazon SageMaker tools, we’re introducing a whole new group of users to the service while also providing additional capabilities for existing customers to make it easier to transform data into valuable insights, accelerate time to deployment, improve performance, and save money throughout the machine learning journey.”

As we noted last month, no-code, low-code platforms are growing in popularity. Such platforms are lowering the barriers to allow data scientists to create applications, or non-data scientists to access advanced ML capabilities.

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