Data Labeling
Market intelligence on solutions, use cases, news, and insights into the data labeling market.
About Data Labeling

In order for many machine learning algorithms to be trained, especially supervised learning algorithms, they need to be fed relevant data that has been appropriately “labeled” with the required output that needs to be learned. For example, image recognition systems that use deep learning neural network approaches need large volumes of clean, normalized image data where the image has been properly labeled as the desired output to train the system over multiple training iterations to build a model that can generalize properly to recognize future images. Such labeling needs to happen for any supervised learning application.