AppSheet adds predictive machine learning to its no-code app development tools
No code application software company AppSheet is expanding its skillset today by adding predictive machine learning capabilities and optical character recognition to its platform to help users make more intelligent business apps.
AppSheet is one of several “no-code” platform providers that enable enterprise workers who lack coding skills to build all kinds of useful software applications via a graphical interface. The new features being released today allow workers to add machine learning processing capabilities to those apps for the first time and speed up data entry tasks.
The predictive machine learning models feature means that applications built with AppSheet can now perform a statistical analysis of the data they create to make better predictions about future outcomes.
“One example of this type of predictive model includes categorizing customer input based on examples of the feedback and the categories to which they belong,” AppSheet officials said. “Another example involves predicting customer churn by using customer instances and data on whether they have ended their relationship.”
Optical character recognition gives apps the ability to read text or handwriting directly from an image, which in turn can speed up data entry. For instance, an app might be able to fill out a form automatically based on the information it sees on an image. Alternatively, an application for scanning conference badges will be able to store data such as the attendees’ name without any human input.
The new features add to AppSheet’s existing artificial intelligence capabilities, which include natural language processing for understanding human speech.
“Our new OCR and predictive model capabilities further demonstrate how any business can easily embed AI and ML capabilities in intuitive software solutions to help drive productivity, without requiring specialized developers or data scientists,” Praveen Seshadri, founder and chief executive officer of AppSheet, said in a statement.
Image: Kreatikar/Pixabay
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