Data labeling or data annotation is a technique that helps computer models to identify and build an accurate understating from the raw data (Text, audio, image, video). This is a crucial part of building supervised machine learning technologies where humans define labels.
Data sets are fed to algorithms and the output is reviewed by humans. There are endless use cases of data labeling. Some common examples include identifying a cat or a bird from an image and identifying a tumor from an x-ray, etc.
How does Data Labeling work?
Computers use labeled and unlabeled data in order to train Machine Learning models. the training data that is fed to the models end up becoming the foundation for machine learning frameworks. Labels enable the selection of optimum data predictions for ML models.
It means data labeling is all about working with labeled data. So first, let’s get understood the difference between labeled data and unlabeled data.
Labeled Data vs Unlabeled Data
There’s a basic difference between labeled and unlabeled data that most people don’t know about. Here’s an easy-to-understand breakdown of the two types of data.
1. Labeled Data
Labeled data as the name suggests is the data that can be named or tagged with one or more labels. The label can be the name of an object, type of object, some number, or some other type of label. Labeled data is incredibly useful for supervised machine learning algorithms.
Examples of Labeled data can be Images of birds, images of trees, images of cars, etc.
2. Unlabeled Data
Unlabeled data is definite as pieces of data that have no labels or targets to predict, only features that represent them.
For example, a list of emails.
Common Types of Data Labeling
There are types of data labels that you should be aware of. Here's the breakdown:
1. Computer Vision
Computer vision enables a computer system to perform tasks that require visual perception. It helps a computer to mimic human sight. Common computer vision use cases are object detection (classification, identification, and verification), and image segmentation. This type of data labeling is used in situations like computer security, surveillance, quality inspection, cars, drones, and automated retail stores. All these situations require computers to understand things visually.
2. Natural Language Processing
Natural Language Processing (NLP) is a technique where a computer learns to understand spoken and written human language. In Natural Language Processing, human language is isolated into fragments in order to understand and analyze the grammatical structure of sentences and meaning of the every word. This helps computers understand spoken and written text in the same way humans do.
3. Audio Processing
Audio processing helps in converting all kinds of sounds into structured data so it can be used by machine learning technologies. In audio processing, you are supposed to require to provide a manual transcribe it into written text.
You can provide deeper information about the audio by adding labels to the audio and categorizing it accordingly. This categorized audio becomes your training dataset.
What are the Benefits of Data Labeling?
1. Data Labeling Helps to Improve the Accuracy of Data
One of the biggest benefits of data labeling is that it helps in increasing the accuracy of data used in the training model.
To make the machine learning algorithm more accurate, they need to be fed more variety of data sets in the training period. Doing this will help the model to learn more accurately, and come up with crucial results in every scenario.
2. Data Labeling Improves the Quality of Data
When it comes to machine learning so nothing is more important than quality training data. Using good data labeling techniques helps you in improving the quality of training data in an interactive manner. Keep in mind that training machine learning models through labeled data takes less time and offers better outcomes.
3. ML and AI models totally rely upon Labeled Data for Accuracy
Machine learning and artificial intelligence technology are skyrocketing. But perhaps there would have been no hope if there was no data labeling technology because, in ML and AI, nothing is more essential than quality that’s why accurate data labeling is very essential in machine learning and artificial intelligence development.
Challenges in Data Labeling
Data labeling has some challenges such as:
- Expensive and time-consuming
However, data labeling is essential for machine learning and artificial intelligence but in spite of that, it can be costly in the terms of both a resource and time perspective. Engineering teams need to set up data pipelines prior to data processing, and manual labeling will almost always be expensive and time-consuming.
- Prone to Human-Error
Sometimes, because of coding errors and manual entry errors can decrease the quality of data. Ends up, it can be turned into inaccurate data processing and modeling.