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Baidu Takes No-Code Approach to Creating Machine Learning Models
Chinese AI specialist Baidu launched a no-code platform for creating custom machine learning models.
Called EZDL, the custom image recognition tool seeks to wed the simplest of development techniques to one of the most cutting-edge technologies.
Using the drag-and-drop approach favored by low-code and no-code dev tools, EZDL employs a four-step process for project development and deployment: create a model; upload and label images/objects; train and test the model; deploy the model with a cloud API or offline SDK.
The company said the tool targets small and medium-size enterprises.
"Even if you have had no exposure to programming, you can quickly build models on this platform with zero barriers," Yongkang Xie, tech lead of Baidu EZDL, said in a statement. "EZDL can help companies with limited AI experts and computing resources to quickly and efficiently complete deep learning training and deployment with only a small amount of data."
Baidu is highlighting use cases in three categories:
- An image classification model for the automatic classification of images by custom label, for tasks such as: classification of home decorating images; image recognition of Chinese herbal medicine, wild birds and frogs; industrial quality inspection to identify defective products; and pavement damage monitoring.
- An object detection model for the automatic detection of objects in images and counting the number of objects by label, for tasks such as retail inspection and medical detection for cell counting.
- A sound classification model for recognizing types of sounds or detecting classes of a condition/event, for tasks such as security monitoring and scientific research.
The company said the new tool is a furthering of its effort to democratize access to AI capabilities, building on its Baidu Brain offerings -- a full AI tech stack including chips, deep learning frameworks and platforms -- which recently shipped in version 3.0 after debuting two years ago.
More information on the new low-code tool can be found in a FAQ.
About the Author
David Ramel is an editor and writer at Converge 360.