What Is Data Labeling For Machine Learning?


Artificial intelligence, or AI, is a phrase that you will have heard many times of the last few years, and it is a phrase that is often used in conjunction with machine learning. However, AI is often mistaken as something from a science fiction movie that will see computers taking over the world. We still control the machines, but we allow them to learn and develop, which helps them to improve and become better, and a process that enables this is data labeling.

What Is Data Labeling Used For?

When it comes to machine learning, the process of finding and tagging data samples is called data labeling, and it is a process that can be done manually, or you can also use software to assist. It allows for both the input and output data to be labeled and classified, allowing the machines to learn and use this information for future processing and is an essential part of the data pre-processing process.

An Example Of Data Labeling

One way to put this into context is to look at a machine learning system that is being trained to identify animals. One way that you can do this is to input images of various animals to the system, allowing it to learn the standard features that animals have, and using this information to classify other potential animals that are in unlabeled images. Having this information enables the system to make an educated assumption as to whether the image has an animal in it or not. As such, data labeling is an essential part of machine learning and AI, and without it, the task would be impossible.

A Large Amount Of Data Required

The process of machine learning can be a relatively slow one, as there is an immense amount of information needed to be able to let the machines learn what you want them to. To enable the machines to learn, all the data that is input to the system will have to have labels, or be annotated, for the computer to have enough information to understand the task at hand. As such, before you can let the algorithm run and start the learning process, you will need to input a vast amount of data as a starting point, no matter what you are trying to teach the system.

The Possibilities Are Endless

The possibilities of machine learning are almost limitless, and it is very prevalent in many businesses today. It can help to spot trends in financial services, shopping, energy usage, manufacturing, and healthcare to name but a few areas, and it is something that is going to get much more prominent as technology advances. With machines and systems becoming smarter and more useful on almost a daily basis, not of it would be possible without data labeling.