What is Data Annotation and how is it used in Machine Learning?
What is data annotation? And how is data annotation applied in ML? In this article, we are delving deep to answer these key questions. Data annotation is valuable to ML and has contributed immensely to some of the cutting-edge technologies we enjoy today. Data annotators, or the invisible workers in the ML workforce, are needed more now than ever before.
Modern businesses are operating in highly competitive markets, and finding new business opportunities is even harder. Customer experiences are constantly changing, finding the right talent to work on common business goals is also an enormous challenge, yet businesses want to perform the way the market demands. So what are these companies doing to create a sustainable competitive advantage? This is where Artificial Intelligence (AI) solutions come in and are prioritized. With AI, it is easier to automate business processes and smoothen decision-making. But, what exactly defines a successful Machine Learning (ML) project? The answer is simple, the quality of training datasets that work with your ML algorithms.
Having that in mind, what amounts to a high-quality training dataset? Data annotation. What is data annotation? And how is data annotation applied in ML?
In this article, we are delving deep to answer these key questions, and is particularly helpful if:
- You are seeking to understand what data annotation is in ML and why it is so important.
- You are a data scientist curious to know the various data annotation types out there and their unique applications.
- You want to produce high-quality datasets for your ML model’s top performance, and have no idea where to find professional data annotation services.
- You have huge chunks of unlabeled data, have no time to gather, organize, and label them, and in dire need of a data labeler to do the job for you, ultimately meet your training and deploying goals for your models.
What is Data Annotation?
In ML, data annotation refers to the process of labeling data in a manner that machines can recognize either through computer vision or natural language processing (NLP). In other words, data labeling teaches the ML model to interpret its environment, make decisions and take action in the process.
Data scientists use massive amounts of datasets when building an ML model, carefully customizing them according to the model training needs. Thus, machines are able to recognize data annotated in different, understandable formats such as images, texts, and videos.
This explains why AI and ML companies are after such annotated data to feed into their ML algorithm, training them to learn and recognize recurring patterns, eventually using the same to make precise estimations and predictions.
The data annotation types
Data annotation comes in different types, each serving different and unique use cases. Although data annotation is broad and wide, there are common annotation types in popular machine learning projects which we are looking at in this section to give you the gist in this field:
Semantic annotation entails annotation of different concepts within text, such as names, objects, or people. Data annotators use semantic annotation in their ML projects to train chatbots and improve search relevance.
Image and Video Annotation
Let’s say this, image annotation enables machines to interpret content in pictures. Data experts use various forms of image annotation, including bounding boxes displayed on images, to pixels assigned a meaning individually, a process called semantic segmentation. This type of annotation is commonly used in image recognition models for various tasks like facial recognition and recognizing and blocking sensitive content.
Video annotation, on the other hand, uses bounding boxes, or polygons on video content. The process is simple, developers use video annotation tools to place these bounding boxes, or stick together video frames to track the movement of annotated objects. Either way deemed fit by the developer, this type of data becomes handy when developing computer vision models for localization of object tracking tasks.
Text categorization, also called text classification or text tagging is where a set of predefined categories are assigned to documents. A document can contain tagged paragraphs or sentences by topic using this type of annotation, thus making it easier for users to search for information within a document, an application, or a website.
Why is Data Annotation so Important in ML
Whether you think of search engines’ ability to improve on the quality of results, developing facial recognition software, or how self-driving automobiles are created, all these are made real through data annotation. Living examples include how Google manages to give results based on the user’s geographical location or sex, how Samsung and Apple have improved the security of their smartphones using facial unlocking software, how Tesla brought into the market semi-autonomous self-driving cars, and so on.
Annotated data is valuable in ML in giving accurate predictions and estimations in our living environments. As aforesaid, machines are able to recognize recurring patterns, make decisions, and take action as a result. In other words, machines are shown understandable patterns and told what to look for – in image, video, text, or audio. There is no limit to what similar patterns a trained ML algorithm cannot find in any new datasets fed into it.
Data Labeling in ML
In ML, a data label, also called a tag, is an element that identifies raw data (images, videos, or text), and adds one or more informative labels to put into context what an ML model can learn from. For example, a tag can indicate what words were said in an audio file, or what objects are contained in a photo.
Data labeling helps ML models learn from numerous examples given. For example, the model will spot a bird or a person easily in an image without labels if it has seen adequate examples of images with a car, bird, or a person in them.
Data annotation is valuable to ML and has contributed immensely to some of the cutting-edge technologies we enjoy today. Data annotators, or the invisible workers in the ML workforce, are needed more now than ever before. The growth of the AI and ML industry as a whole depends solely on the continued creation of nuanced datasets needed to create some of ML’s complex problems.
There is no better “fuel” for training ML algorithms than annotated data in images, videos, or texts – and that is when we arrive at some of the autonomous ML models we can possibly and proudly have.
Now you understand why data annotation is essential in ML, its various and common types, and where to find data annotators to do the job for you. You are in a position to make informed choices for your enterprise and level up your operations.