Data labeling in machine learning is the act of identifying raw data (images and text files, etc.). To provide context for machine learning models to learn from the data, it is necessary to add one or more meaningful and informative label(s). You can also read the full info here about data labeling.
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Labels could indicate whether a photo is of a bird or a car, what words were used in an audio recording, or if an image contains a tumor. Data labeling is necessary for many purposes, including computer vision, speech recognition, and natural language processing.
How does data labeling work?
Most machine learning models today use supervised learning. This is an algorithm that maps one input to one output. Supervised learning works best when there is a set of labeled data from which the model can draw correct conclusions. Data labeling is usually done by asking humans to make judgments about unlabeled data.
Labelers might be asked to tag every image in a data set that contains "does it contain a bird?" Tagging can be as simple as a yes/no answer or as detailed as identifying specific pixels within an image that are associated with a bird.
Machine learning models use human-provided labels to discover the underlying patterns. This is called "model training". This results in a trained model that can make predictions on new data.
How can data labeling be accomplished efficiently?
Large amounts of high-quality training data are the foundation of machine learning models that work well. The process of creating the training data required to build these models can be costly, time-consuming, and complicated.
Most models today need a human to manually label the data so that they can learn how to make the right decisions. This problem can be overcome by using machine learning models to automatically label data.
