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Why Is Data Labeling Important For Machine Learning?

Data Labeling

For the past ten years, artificial intelligence (AI) has received a lot of attention. The use of robot assistants and automated industrial processes has simplified many occupations and people’s lives. So, Engineers are trying to create an AI model to identify objects accurately. The simplest technique to create AI model for identifying items is to establish a deep ML model. The eliminates the coding for representing an object. For this, they utilize tagged photos to feed into the training of an AI model. Moreover, data labeling is used to help machine learning models understand and recognize objects.

What is Data labeling?

Data labeling is the method of identifying the raw data and assigning appropriate labels or tags. This method is applied to data for explaining what the data is about, enabling ML models to make accurate predictions. Raw data, including pictures, videos, texts, and audio, can be tagged or given labels. These tags serve as a representation of the object class to which the data belongs. It further aids ML algorithms in learning to recognize particular object class when it appears in data without a tag. Data labeling is essential for a number of use cases, including speech recognition, computer vision, and natural language processing.

Use Cases of Data Labelling

Computer vision

A picture is worth a thousand words. Due to this, people are keen to teach machines to understand visual content. For AI to see the world how we see it, it’s necessary to show the machine thousands of examples and let it learn. Computer vision develops models for extracting valuable information from images, videos, or other visual inputs. This is accomplished by utilizing training data that allow computer modesl to recognize important details in pictures and locate objects.

Natural Language Processing

NLP, or natural language processing, is a subfield of artificial intelligence that enables computers to comprehend spoken language. NLP integrates languages, statistics and ML to study the structure of language for creating sophisticated text and speech understanding systems. To create training dataset for NLP, individuals pick out key passages in the text, create relevant labels and add tags.

Audio processing 

Audio processing converts different kinds of sounds into a structured format for identifying speech, animal noises, and construction sounds. Manual transcribing of the audio into written language is the initial step. Tags are then added for categorizing and locating specific audio information. This categorized and labeled dataset can be utilized as a training set.

Best practices for data labeling

There are several techniques for improving the efficiency and accuracy of data labeling. Here are some of these techniques: 

Active Learning

It is advised to employ automatic data labeling when working with big datasets. This can be accomplished with a machine learning technique called active learning. The active learning approach improves efficiency of data labeling by selecting the most relevant dataset to be labeled by humans. This is done using various ML algorithms and semi-supervised learning.

Label Auditing

The label auditing technique is used to check the accuracy of labels and update them as needed.

Transfer Learning 

Transfer learning increases the effectiveness of data labeling by reusing previously completed tasks to produce hierarchical labels. One or more previously trained models from one dataset are applied to another using transfer learning. This can involve multi-task learning, in which several tasks are learned simultaneously.


This method determines the agreement rate between various labelers—human or automated—on the provided dataset. The consensus score is determined by dividing the total number of agreeing labels by total number of labels per asset.

Intuitive and streamlined task interfaces

Intuitive and streamlined task interfaces reduce the cognitive load and context switching human labelers require. It decreases mental strain of data labeling by enabling human labelers to switch between tasks rapidly and without getting frustrated.

Benefits of Data labeling

Data labeling improves the context, quality, and usability of data for individuals, teams, and businesses. Data is crucial for the model; giving it poor data would lead to unexpected results. However, having accurate data labeling improves quality control in ML algorithms, enabling the model to produce desired results. Data labeling techniques increase the effectiveness of the data in a model. For instance, the categorical variables are reclassified as binary variables to make them more consumable for a model. Furthermore, data aggregation improves the model by lowering the number of variables or making it possible to include control variables.


Data labeling enables AI and ML algorithms to develop a precise grasp of conditions that exist in the real world. Data labeling tools enable collaboration between humans and machines to produce accurate and effective data for diverse ML applications. Businesses can use our Machine Learning Consulting Services to implement machine learning solutions in their organizations. Our services assist in producing precise labels for a collection of data that organizations may employ in machine learning models.