Better Machine Learning Demands Better Data Labeling: Key Takeaways Better Machine Learning Demands Better Data Labeling: Key Takeaways
Machine learning (ML) techniques have had a huge societal impact in many cases and applications such as speech processing, natural language... Better Machine Learning Demands Better Data Labeling: Key Takeaways

Machine learning (ML) techniques have had a huge societal impact in many cases and applications such as speech processing, natural language comprehension, neurosciences, health, and the Internet of Things (IoT). The advent of the big data age has given a great deal of impetus to machine learning. ML algorithms have never been better promised and have been challenging data to gain new insights into different business applications and human behavior.

On the one hand, big data provides unprecedented information for ML algorithms to extract underlying material patterns and creation of predictive models; on the other hand, traditional ML algorithms are critical challenges such as scalability to use the dig data to its most extent. With the ever-expanding universe of big data, ML must grow and evolve to turn big data into its functional intelligence. We need high-quality data to create good models. However, collecting and labeling a large amount of high-quality data is time-consuming and costly. Data also needs to be converted, and only then will it become a valuable asset in building models.


What is Data Labeling?

Data labeling can be described as the process of tagging or raw tagging data, such as images, videos, text, and audio. These tags represent the class of object data and help the machine learning model to identify that particular class of objects when they are encountered in the data without a tag.

Computers cannot process visual information the way the human brain does: decisions must tell the computer what it interprets and provide context. Data labeling creates these connections. The human-driven task is to tag content such as text, audio, images, and video so that machine learning models can recognize it and use it to make predictions.

Working of Data Labeling

ML and in-depth learning systems often require huge amounts of data to provide a basis for trustworthy learning methods. The data these processes use to inform learning should be labeled or unmarked based on data functions that help the model organize the data into patterns that provide the desired response.

The tags used to identify the data identifiers must be informative, distinctive, and independent in order to create a quality algorithm. Properly labeled data provide the comprehensive truth that the ML model uses to verify the accuracy of its predictions and to improve the algorithm. The high-quality algorithm is high in terms of both accuracy and quality. Accuracy refers to the closeness of certain tags in the data to the truth. Quality refers to the accuracy of all data.

Methods of Data Labeling

There are various methods being followed by various organizations across the world using ML. Here are some of the most common data labeling methods for your better understanding.


Instead of hiring temporary staff or relying on a crowd, you can turn to outsourcing companies that specialize in preparing training data. Outsourcing organizations position themselves as an alternative to joint procurement platforms. Companies emphasize that their professional staff provides quality training data.

Machine-Based Labeling

One of the newer forms of labeling is machine-based labeling. Machine-based labeling refers to the use of annotation tools and automation, which can dramatically increase the speed of data annotation without sacrificing quality. The good news is that recent developments in the automation of traditional machine tooling tools using unattended and semi-supervised machine learning algorithms have significantly reduced the workload of human markers.


In this process, the data labelers of your team behave as data researchers. This approach has a number of immediate advantages: it is easy to monitor progress, and the accuracy and quality are reliable. However, outside large companies with in-house data science teams, in-house data tagging may not be a wise choice.


Crowdsourcing can be described as the process of obtaining labeled data with the assistance of a large number of freelancers registered on a joint procurement platform. Annotated data sets usually consist of trivial data, such as images of animals, plants, and the natural environment, and do not require additional knowledge. Therefore, the addition of simple data annotations is often directed to platforms with tens of thousands of registered data annotators.

Why is Data Labeling Important?

Manual labeling of data is the most time-consuming and costly method but may be justified for important applications. Critics of artificial intelligence suggest that automation is jeopardizing low-skilled jobs such as call center work trucks and Uber driving. It is simpler for various machines to perform fewer menial tasks. However, some experts believe that data tagging can provide a new low-skilled job opportunity that will replace jobs that have been reset with automation, as the surplus of data and machinery needed to perform the tasks required for their work is constantly growing. 

Final Takeaway

If the labeling process presents you with problems when creating your next machine learning project, use active learning to minimize the number of tagging tasks. You can also use pre-trained deep neural network outputs to convert your tasks from raw data to vectors. In the process, companies can also use a combination of information measures to select the following training examples, reduce model uncertainty, and promote representativeness and diversity.

About Author 

Rebecca Williams is the Senior Technical Content Writer at Matellio who loves and finds passion in discovering vital technology insights. She enjoys implementing various writing styles and techniques. She is an engineer, which gives her a broad understanding of various tech tools and platforms.

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