Machine learning (ML) is one of the most popular subjects in the tech world today. It involves training systems to make decisions based on a specific data set. This branch of artificial intelligence has become an essential part of modern technology, like smart homes, cybersecurity, and online shopping.
Before a machine accurately makes decisions based on a database, the data needs to be annotated. The labeled data allows the system to distinguish items based on their names and other unique features. If you have a data-related project, one of the main things you should invest in is a data annotation tool. It’s basically a platform dedicated to making your data labeling projects successful. (1)
This article discusses the basic meaning of data annotation, how it’s done, and why this technology matters.
Table of Contents
What is data annotation?
Data annotation in machine learning refers to the process of labeling files in different formats, like images, videos, and texts. This data in its raw form isn’t machine-readable. For instance, a computer can barely distinguish between a photo of an animal and a car unless the two are labeled, so image tagging and annotation services are used for this.
It’s worth noting that over 90% of data is usually unstructured. That means most of the photos and videos you take or the texts you write aren’t properly defined. Therefore, if you’re an AI/ML initiative, you’ll need to properly annotate the data so the algorithm will be able to classify and understand whatever information you’re feeding the machine.
Image, audio, video, and text annotation are the four main types of data tagging. You’ve probably used image and video annotations while taking or editing pictures with your smartphone’s camera software. Audio and text labeling is also important in today’s world, where computers are tasked with a significant percentage of work that humans use to accomplish manually. (2)
What is the process of data annotation?
Once you have everything in place, you can start labeling your data. The annotation process basically follows the following steps:
Data acquisition
The first stage of the annotation process involves the collection and aggregation of data. Before starting your machine learning project, you’ll need a set of files that’ll be used in training the machine. This data includes text files, raw videos, images, and audio.
Of course, the files you choose to use will depend on your project and what you plan to achieve. After collection, the data will be cleaned. During this process, any file that’s not properly formatted will be changed to the correct format. Corrupted and duplicated files will also be removed before the data can be fed to the labeling model.
Data labeling
The actual data labeling is the central step of this whole process. Here, all the clean collected files will be analyzed and tagged according to their specific contexts.
For example, suppose a file is associated with a given location or personality. In that case, you can tag those unique details so the AI system can use them to distinguish various images, videos, audio, or texts. This information will come in handy in the future when the machine is expected to respond to different relevant queries.
Quality check
The final step is ensuring that the data has been tagged with precision. Remember, the quality of the whole process is determined by the accuracy of the coordinate points of what’s known as the bounding box. In other words, it should be within the acceptable range of the margin of error.
There are several quality assurance algorithms that can be used to determine the accuracy of the tags. These data annotation tools include the consensus algorithm and Cronbach’s alpha test. Their basic role in this process is to measure reliability and the level of data dependability.
Why does data annotation matter?
Technology has become part and parcel of every individual today. As such, companies are working hard to ensure that they customize their products to meet their business needs and those of their potential customers.
Artificial intelligence makes this possible by ensuring that the automated decision-making process is efficient enough. However, for this to work uniquely for your company, the data used in your system must be labeled, which is why data annotation is very crucial.
It’s expected that AI and ML will play a vital role in economic activities by 2030. With a current growth rate of 28.4%, you can see why data annotation and other ML-related projects are very important for the future of every business. Hence, it’s best to know some essential features to consider before choosing data annotation tools for your company. (3)
Conclusion
The use of machine learning has been growing at a rapid rate over the past few years, and that’s set to continue. At the heart of this technology is data annotation, which has also gained a lot of popularity recently. It involves tagging and labeling data files to make it easier for the machine to understand their meanings and contexts.
Data labeling can be divided into four types: image, video, audio, and text annotation. All of these types, however, go through the same stages. The first step is data collection, followed by the labeling process, and, finally, quality assurance.
References
(1) “Data Labeling Service: How to Ensure Data Quality for Machine Learning and AI Projects?”, Source:
(2) “The One, Two, Threes of Data Labeling for Computer Vision”, Source: .
(3) “How the Global Demand for Data Labeling is set to Increase?”, Source:
