A driverless car should be faultless – there is no room for error.
The ability of a driverless car’s accuracy is improved only if the data of the car has been labeled under parameters such as sizes, signs, colors, shapes, and angles.
The point here is, where can we get such kind of data?
Today, data labeling has become an industry of its own.
Ever since the 2010s, we’ve seen multiple companies making huge investments in machine learning. Precisely, Supervised Learning now becomes one of the most commonly used forms of machine learning by many industries. Supervised learning algorithms are supposed to be fed with labeled instances thus accelerating the significance of labeling solutions.
As a result, data labeling tools and service providers have become critical solutions toward an organization’s strategy.
What is labeled data?
In terms of machine learning, if your data is labeled, it simply means that the data you’re using is marked up, or annotated (data is processed) to demonstrate the target which answers you want your machine learning model to predict.
Simply put, data labeling refers to tasks that involve data annotation, tagging, classification, transcription, processing, and moderation.
A simple explanation –
A labeled data is a group of multiple datasets that gets tagged with more than one label to identify specific properties or characteristics or classification of objects. Now this labeled data is further consumed in the machine learning model to train it up to a certain level of accuracy. So, when this labeled data is fed into the trained model, it predicts the exact characteristics required to make final predictions.
The role of a data labeler
The manual arrangement done by humans on AI applications and machine learning is data labeling. Labeling of data is crucial because computers are bound by multiple limitations. Most importantly, not all of them can be without human intervention.
A computer system can be programmed to perform activities that do not need the human hands, however, the same program will not be able to distinguish between a dog and a cat without training the computer. Therefore, the need for algorithms to learn based on the dataset provided which also requires supervision.
In short, supervised machine learning. It is called so because computers need human supervision in order to get trained to execute tasks that can be challenging for machine learning, but easy for humans. Thus, the need for a data labeler.
Data labeling: an important segment for businesses adopting AI
As humans, we tend to perceive real-world atmosphere by observing things through our eyes, which is understood by our brains, thus making us learn what we see.
It is the same with machine learning opening new avenues toward business environments. For example, data labeling helps reduce operational costs, detects false insurance claims, and speeds up the mechanical processes, etc.
Despite being technologically advanced, yet our most important struggle remains the same –making sense out of avalanche data that is being generated every second.
- Multiple security cameras have been installed yet there are unable to alert us when a bank is about to be robbed.
- Drones were everywhere across the Amazon rainforest, yet it failed to track the climatic changes happening every year.
A humongous amount of data is generated every second in the form of an image, email, video, text messages, or audio. Yet our most advanced machines still find it tough to understand and manage a large amount of data.
In a nutshell, we’re under construction of giving vision to our smartest machines.
Methods of labeling data
Organizations can use multiple methods to label their data. These options could range from using data labeling services to in-house staff and crowdsourcing.
- In-house staff – organizations can use their existing staff to process data.
- Crowdsourcing – being a third-party platform allows organizations to gain access to multiple workers at once.
- Managed teams – organizations have the option of enlisting a managed team just to process data. Such teams have been trained, evaluated, and managed by third-party companies.
- Contractors – if needed, an organization can easily hire temporary freelance workers to label and process data.
There is no sure shot of labeling data. Organizations can use any method that best suits their needs. However, factors such as the company’s size, size of the dataset that needs labeling, financial restraints of the enterprise, and the skill level of the employees should be considered while labeling data.