Learning the Basics: A Quick Guide to Data Labeling

November 4, 2022

| Carolyn Joy V.

Artificial intelligence (AI) subfields such as machine learning (ML) and deep learning rely heavily on data — massive amounts of it, in fact. But while there is no shortage of data available from the web, transactions, machines, and other traditional sources, the huge challenge lies in making sense of all that data. This is where data labeling can prove to be very valuable.

In this blog post, we hope to give you a simplified guide to data labeling that offers the basics of what you need to know so you get high quality labels for your organizational data.

What is Data Labeling?

Data labeling is the process of detecting and adding tags to raw data samples — images, text, audio files, videos, and others — so that ML algorithms can then learn from them. Informative labels in machine learning can provide more context and meaning to the data, allowing ML models to improve accuracy of predictions and estimations. The entire data labeling workflow generally includes tasks such as data tagging, annotation, classification, moderation, transcription, and processing.

Understanding Labeled and Unlabeled Data

Now just because a piece of data is classified as unlabeled doesn’t mean that it’s rendered unusable. Both labeled and unlabeled data can be utilized for machine learning models, albeit in varying levels of usability.

Labeling tool

A very simple example would be if a machine learning algorithm is being developed to differentiate three common animals, say a cat, dog, and rat. Labeled datasets that have properly tagged images of these three animals would allow the program to identify and classify them immediately. When unlabeled images are fed to the program however, the algorithm would have to classify them according to their properties, e.g. color, body shape, characteristics of ears, eye features — you get the picture.

Based on the above illustration, you can see how essential having labeled data is for building a high-performance ML model that delivers accurate results.

Approaches for Data Labeling

Considering how crucial a quality label in machine learning is to developing an effective algorithm, organizations have to carefully consider the right path to efficient data labeling. Here are five common data labeling approaches:

Synthetic data generation

Leverage Data with Data Labeling

Building successful ML models can only be done effectively when they are fed with massive amounts of high-quality labeled data. Whether your enterprise annotates with inhouse experts, uses programs and scripts, or crowdsources/outsources to data labeling platforms, it’s important to understand that machine learning and other AI algorithms can only be as good as the data they are trained with.

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