Are you aware of the report claiming that the data labelling tools market was approximately USD 700 million in the year 2019? Moreover, researchers expect that this market will grow to USD 5.5 billion by the year 2026. Can you imagine how rapidly the data labelling market is growing nowadays?
All this is happening just because the data annotation projects add considerable value to the companies and boost their return on investment. It is the main reason you should also consider hiring top AI data labelling companies to get labelled datasets for training your machine learning model.
But the moment you think of taking the first step, a fundamental question might cross your mind – what is the average data labelling service cost, right?
So, for that, we have collected some crucial data for you that will give you a rough idea of data labelling service cost. But before sharing any further details, please wrap your mind around data labelling and its most common types.
Table of Contents
What is Data Labelling?
In layman’s terms, Data Labelling is a widely adopted data science activity where annotators add meaningful comments to the text, images, and other visuals to create labelled datasets and then use them for training machine learning models.
When tagging the unlabelled audio and video files, it is imperative to keep accuracy in mind. The reason? It will prevent your ML-powered model from making wrong predictions and produce incorrect results down the road.
What are five common types of Data Labelling?
1. Image Labelling
Suppose that you want to train an ML-driven image classification model to recognize if some pictures contain dogs. Then, you will have to train it with a set of images labelled as “Has dogs” and “No dogs.” The purpose of doing this is that the machine learning models rely on the labelled data when you train them to perform accordingly down the line.
2. Audio Labelling
In another case, you might want to use audio files for your machine learning model development. For instance, you want to create a unique application for animal watchers to identify various dog species from their barking sounds. Then for that, you will need a massive training set of the dog barking audio files in which each file should have labelled with the dog it belongs to actually.
3. Video Labelling
In this data labelling type, we take an example of an online video service provider. Suppose that they want to show advertisements for dog food each time someone plays the video containing dogs. So, for that, they will have to train their scientific model with the videos labelled as “Contains Dogs” and “No Dogs.”
4. Structured Data Labelling
In this part, we will consider an insurance company. Assume that they want to develop a classification model to classify the probability of whether a claim should get paid or not. In that case, they will have to acquire a training dataset that contains all the necessary information associated with the claims and then teach their ML model with the “Paid” and “Not Paid” labels.
5. Unstructured Data Labelling
In the last data labelling type, we will take an example of a media agency. Assume that they have a news feed in which they want to classify third-party news articles into different genres such as technology, entertainment, and business. Now, when they have to train their machine learning model for the same, they need training datasets containing many articles and labelled with the genre of that content piece.
So far, you have learned the definition of data labelling and its common types. Now let’s see what determines the data labelling service cost.
How do service providers decide the Data Labelling and Annotation prices?
If you want to know the answer to this question in one line, we must say that the prices for data labelling and annotation depend on the complexity and volume of the datasets.
Further, you also need to consider what kind of data annotation services you want, like text, image, or video, and what annotation techniques you want labelling professionals to adopt for annotating your desired data.
All this expenditure is part of your data annotation project, and you must factor in all of them when preparing the budget for your ML model training and development.
For instance, if an annotator uses the Bounding Box Annotation method to label a specific dataset, they will have to invest less time and effort in it than Semantic Segmentation.
The reason? The latter approach requires more experience to do the job and taking extra precautions during image annotation. It’s because when they have to annotate the outline of a particular object in an image, they need to avoid every possible error to ensure that the picture is recognizable to machines through computer vision.
We hope you now know what data labelling is, its types, and its cost. So, if you want to leverage accuracy-centric data labelling services now, don’t delay reaching out to the market-disruptive data labelling companies out there.
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