Data Labeling, or commonly called Data Annotation, is an important point in Machine Learning. To train AI from data, it is imperative to label this data beforehand. Machine Learning enables computers to learn independently, training from data. However, for machine learning to begin, humans must intervene. Before feeding the AI model with data, it is essential to prepare it.
Using various tools, labels are assigned to the data. This is what will then allow the computer to learn to recognize the different categories and to distinguish them. This is “Data Labeling”.
This task does not require special technical skills but requires a lot of time. Most companies outsource this work, to stay focused on their core business. There are many Data Labeling service providers.
A computer is programmed to perform complex calculations. It can automate activities. Yet this same machine would be unable to distinguish a dog in a photo. This task may seem easy for us, as this ability is almost innate for a human. To achieve this, a computer must be trained to do this job. It needs to use an algorithm to learn from a set of data. And that data needs to be labeled.
Tagged data is annotated data, to present the “target”. This is the answer, which we want the machine learning model to learn to predict. In reality, Data Labeling refers to different tasks in addition to data annotation. These include classification, moderation, transcription, and processing.
Data labeling highlights the characteristics of the data, their properties, and their classifications. Analysis of these characteristics helps predict the target. Take the example of a machine learning computer vision model for an autonomous vehicle. Frame-by-frame video labeling tools can be used for data labeling. Labels will be used to indicate road signs, pedestrians, or other vehicles.
The person responsible for labeling the data is called a “human-in-the-loop”. This is the Data Labeler. Its labels allow the machine to identify the elements presented by the data. They are essential for developing high-performance algorithms. Once trained, a model will allow for example pattern recognition, classification, or regression.
To develop a Computer Vision system, it is necessary to label the images to create a training dataset. It is possible to classify the images by content or by quality. The data is then used to train a computer vision model. Once trained, this model will be able to automatically categorize images, segment them, detect the location of specific objects, or identify key elements.
In the field of Natural Language Processing, it is first necessary to manually identify important sections of a text or to label it with specific labels to constitute a training dataset. The goal may be to identify the sentiment or intention of a text, to identify parts of speech, to classify proper nouns, or to identify text in pictures or other documents. Boundaries must be drawn manually around text elements. Once trained, a Natural Language Processing (NLP) model can be used for text analysis, name/entity recognition, and optical character recognition.
Audio processing consists of converting all types of sound into a structured form so that it can be used in machine learning. This task usually requires you to first transcribe the sounds into written text. This helps highlight in-depth audio information, add labels, and categorize sounds. Once labeled, this data can be used for training.
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