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Guide to Perfect Data Labeling for Machine Learning

The scope and utility of artificial intelligence has made it one of the most needed technologies at present. Its implementations have helped reduce human errors and efficiently manage numerous systems and processes.

Artificial intelligence (AI) and Machine Learning (ML) models depend on two components - algorithms and data. An efficiently working AI/ML model is supported with a functional algorithm that is developed on the foundation of well-labeled data. In such a scenario, if your data is not labeled properly, it will negatively impact the performance of the AI/ML model. 

Let’s discuss how you can perform perfect data labeling to develop efficient machine-learning models.

Table of Content

  • What is Data Labeling and Why is it Important?
  • Types of Data Labeling
  • Usage of Data Labeling
  • How to Label Data Effectively
  • Conclusion 

What Is Data Labeling and Why Is It Important?

In simple words, data labeling can be defined as marking or addressing raw data in a certain way that helps the machines recognize it. To create artificial intelligence, to train a machine to perform like humans,  we need to input information of respective data. The process of capturing features of raw data like image, text, audio, video, ladder; and encoding it or tagging it with labels is called data labeling.

It helps the machine learning models precisely predict and estimate the data. Through this process, a captured data is converted into machine language in the form of algorithms.

Good data labeling is important as it assists in the working of artificial intelligence by providing actionable information in the form of machine learning algorithms. Lose data labeling will result in the AI missing on certain actions or improper analysis of commands.

For instance, consider the training model for automobile painting. An automobile has parts of different materials that need to be painted differently. Fiber parts would use different kinds of paint than metal parts. Above this there is a color pattern to follow. For your artificial intelligence model to work efficiently each and every part should be labeled against its color, its type and according to the decided pattern.

In case of wrong labeling the machine learning model might miss on a certain part’s color. It may paint a fiber part like metal or miss on the pattern.

Types of Data Labeling

Number-based data is quantitative and structured; it can be analyzed using conventional tools. Whereas unstructured data is typically qualitative, like images, videos, etc. Such data cannot be analyzed using quantitative data methods. Depending on the data type, labeling can be categorized into:

·         Computer Vision Technique

AI applications like face recognition, object detection, image classification, visual relation analysis, instance, and semantic segmentation, etc., are performed through this data labeling style. It includes two kinds of labeling:

·       Image Labeling

Data of images is used to create a program to respond to its contents to achieve required goals.

  • Video Labeling

Video footage is utilized in the form of labeled data to train a machine program and get needed responses.

·         Natural Language Processing (NLP)

Artificial intelligence applications that assist computers in understanding spoken languages utilize NLP-style data labeling. It is achieved by adding tags or bounding boxes to outline labeled text. It can be classified into:

·    Text or Speech Labeling

Machines can be proficient to bring meaning, and define the framework and intention, of textual data by annotating it in suitable language. Text Labeling in NLP can be done complete syntactic or semantic methods.

Usage of Data Labeling

As technology is preferred over human intelligence in tasks that have a repetitive nature, AI can reduce the risk of human error. Data labeling is significantly used in

·         Industrial Robots

Data labeling-based AIs have brought significant efficiency to industrial operations, specifically manufacturing. It is used in defect detection, random sorting, intelligent handling, network security, surveillance, etc. Data labeling has reduced human errors and enhanced quality assurance for industries.

·         Healthcare

Investigation and development have also gained from the advent of artificial intelligence. Conclusion, general surgeries, cosmetic surgeries, medicinal research, and biotechnology use data labeling.

·         Unmanned flying objects

Autonomous aircraft, drones, and other commonly used AI-controlled flying objects utilize data labeling services to set flight targets. It helps them define processes and set goals.

·         Automatic Driving

Motor training institutes and machinery use data labeling to impart lessons on driving.

·         Retail industry

Vision-based inspections, quality control, inventory management, and eCommerce are using data labeling.

·         Security

Data labeling-based AIs help in identifying trespassers, lawbreakers, and dubious activities, as well as identity verification and entry-exit controls.

·         Agriculture

The labor of farmers has reduced significantly with the use of yield-based data labeling. It assists in irrigation, pest control, quality check, and controlling trespassing stray animals.

How To Label Data Effectively

Data labeling can be achieved through

·         In-house human resources

·         Oworkers (Outsourcing data labeling services)

·         Crowdsourcing as per requirement

However the process involves a few features that you should know for efficient data labeling. They are as follows:

·         Collect Diverse Data

·         Collect Precise Data

·         Set The Labeling Standards

·         Set a Quality Assurance Process

·         Give Feedback

·         Run a Pilot Project


Accuracy and quality of data labeling can change the game for your machine learning model. While accuracy is about creating data labels as close to reality as possible so that they can effectively communicate the meaning to AI applications. Quality is about the consistency in your data sets in terms of labeling standards.

Many tools have been introduced for different levels of your machine-learning labeling process. They can help you automate and streamline it. However hiring an expert data labeling service can always do the trick, saving your time and investment.

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