How can businesses benefit from AWS no-code machine learning service?
The AWS no code machine learning solution aims at addressing business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.
Last week of November 2021, Amazon SageMaker Canvas, the latest machine learning service from AWS, was introduced. In contrast to its existing machine learning services, the target audience for this project isn't data scientists and engineers with advanced degrees. It's any engineer or business user inside a firm who wants to leverage artificial intelligence. SageMaker Canvas promises to let anybody create automated prediction models with a simple point-and-click setup.
If you've heard of a similar service, it's probably because Azure and others provide comparable tools. AWS may have the edge since many organizations already store all of their data in AWS, but “SageMaker Canvas leverages the same technology as Amazon SageMaker to automatically clean and combine your data, create hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions,” writes AWS’ Alex Casalboni in the announcement. “It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types let you address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.”
The service is powered by SageMaker, Amazon Web Services' completely managed machine learning service, which should come as no surprise.
The goal is for users to be able to input any dataset, from a simple CSV file up to a sophisticated one, and then choose which columns in the data set Canvas should predict. There's no need to be concerned about how to teach this algorithm. While this is a much simpler user experience than utilizing conventional ML tools, we're not quite there yet. This is, after all, AWS. The overall experience is more akin to using the AWS console than a contemporary no-code app.
SageMaker Canvas is generally accessible in the United States (Ohio, North Carolina, Oregon, Washington), Europe (Frankfurt), and Ireland. You may start utilizing it right away with your existing data, as well as information stored on Amazon S3, Amazon Redshift, or Snowflake. Set up and link your data sets in a few minutes, summarize anticipated accuracy, discover which columns are relevant, train the best performing model, and generate new individual or batch predictions with just a few clicks.