Artificial intelligence (AI) and Machine Learning (ML) are making huge waves across all industries. However, no matter how cutting-edge and groundbreaking, these technologies are only as good as the data that powers them1. As the quality and variety of data continue to increase exponentially, the success of a model greatly depends on how a machine makes sense of it.
This is precisely why data labeling is crucial. It is defined as the task of annotating data—usually in the form of images, videos, audio, and text—with labels. These labels add meaningful values to a piece of data, providing the necessary context for AI models to learn from it better2.
To put it simply, before a driverless car can hit the streets, it needs to go through a rigorous process applying context to specific parameters such as shapes, objects, colors, sizes, angles, and distance. An autonomous vehicle collects data through sensors, which will be used for visual detection and interpretation. However, before a computer vision model can navigate on its own based on the real world data directly it must be properly trained using high-quality data. Once that is done, the model can infer or detect new patterns and classify objects precisely, allowing the car to drive itself.
Data labeling is an integral step in the preprocessing stage of building AI models via supervised learning, where learning algorithms are applied to map one input to an output. It works by having a labeled set of data that the model can learn to make correct decisions. An adequately labeled dataset used as the objective standard to train and evaluate a given model is called the training data.
The accuracy of your training data will determine the accuracy of your trained model thus, putting in great effort and investing in high-quality data3.
Humans play an integral role in the AI model’s supervised learning. The development of the training data is accomplished through data experts as they make judgments on given pieces of unlabeled data4. For example, an expert may be tasked to annotate videos that contain vehicles. Labeling could be as unrefined as a simple “yes” or “no,” or as specific as identifying which individual pixels belong to each object car within the instance segmentation.
However, the role of humans does not end there.
Human intervention must be maintained during the testing phase to provide performance monitoring. Applying a human-in-the-loop (HITL) configuration means continuously involving people in the cycle of processing, judging, and improving the model. Through the means of human judgment, can identify gaps and the necessary insights generated to recalibrate or retrain as necessary5.
The explosion of artificial intelligence, machine learning, and even more so deep learning, means the rise of spending on data labeling.
Nearly all industries can benefit from leveraging AI and its capabilities. The explosion of ML also brings with it the rise of spending on data labeling services. According to Global Market Insights, the data labeling market is expected to expand at a compound annual growth rate (CAGR) of over 30%, reaching around $7 billion by 20276.
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The quality of your AI is only as good as the quality of your data. And fine-tuned technologies are vital to achieving this. However, Teammates with the essential skill sets and expertise ensure that accurate data and insights are served—resulting in an overall better performance model.
Data experts are an integral part of client experience. It is why investing in robust training, industry coaching, holistic wellness, and a positive company culture provides fruitful results. When employees enjoy and take pride in what they do, they go the extra mile and produce incredible results.
Combined with advanced solutions and strategies, well-rounded data experts make up the foundations for how AI Operations cater to the most challenging AI applications.
Here are a few examples of how TaskUs delivered expert AI Operations solutions to our clients:
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