Labeled Data: Labeling Methods in 2025

Introduction to Data Labeling

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Data labeling is an important step in the development of machine learning models, serving as the bridge between raw data and actionable insights. By assigning meaningful labels to raw data—whether it’s images, text, or audio recordings—data labeling enables machine learning algorithms to interpret and learn from the information. The quality of these labels is directly linked to the performance of machine learning models, as high quality labeled data is essential for making accurate predictions.

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There are various types of data labeling methods, each suited to different types of data and project requirements. However, the process is not without its challenges, including ensuring consistency, managing large volumes of data, and maintaining data quality. Understanding the importance of data labeling and the available methods is key to building reliable and effective machine learning solutions.

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Data Labeling Process

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The data labeling process involves several key steps that transform raw data into valuable training data for machine learning. It begins with data collection, where raw data is gathered from diverse sources such as sensors, databases, or user-generated content. Next, data preprocessing is performed to clean and format the data, ensuring it is ready for the labeling process. The core step is label assignment, where meaningful labels are attached to each data point.

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This can be done through manual labeling, where human annotators carefully assign labels, or through automated methods that use machine learning algorithms to assign labels automatically. The choice between manual and automated labeling depends on the complexity of the labeling task, the type of data involved, and the resources available. A well-structured data labeling process is essential for producing high quality labeled data that can drive effective machine learning outcomes.

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Types of Data

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In machine learning, understanding the types of data in a training dataset is fundamental to choosing the right approach for model training. Data can be broadly categorized into labeled and unlabeled data. Labeled data consists of data points that have been annotated with meaningful labels, making it suitable for supervised machine learning, where models learn from these examples to make predictions. Unlabeled data, on the other hand, lacks these annotations and is often used in unsupervised machine learning, where algorithms seek to find patterns or groupings within the data without predefined labels.

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There are also semi-supervised approaches that leverage both labeled and unlabeled data, combining the strengths of each to improve model performance. Selecting the appropriate type of data and labeling strategy is essential for building robust machine learning models.

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3 Data Labeling methods for your AI models

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Data Labeling is an essential process in the field of machine learning. It consists in associating labels or labels to data, in order to make them usable by machine learning algorithms (Machine Learning or Deep Learning). “Powered” by these processed and enriched data, an AI prediction model can learn to perform a specific task, such as recognizing speech in a defined language or detecting objects in an image (example: detecting vehicles on a highway).

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‍Object detection is a Computer Vision task that involves identifying and localizing objects within images, often requiring the annotation of key points to mark critical features or landmarks for training computer vision models. Labeled examples are paired with correct answers and are used to build training datasets and data sets, which are essential for training machine learning models and improving their accuracy and efficiency.

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There are several Data Labeling methods, each with its own pros and cons. Some common examples include:

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1. Manual Data Labeling (or Manual Labeling)

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This is the most common and easiest method. It consists in "using a human" to label data manually. This method is particularly useful for low-quality data (a set of fuzzy images that require human interpretation) or for complex tasks that require human reflection, understanding, or subtle interpretation. Data labeling work often involves assigning labeling tasks or data labeling tasks to human annotators, who are responsible for accurately categorizing and annotating the data. Internal labeling is often used for sensitive or specialized data, where in-house teams handle the annotation process.

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To assist or automate parts of the manual process, labeling functions or a labeling model can be employed, enabling more efficient and adaptable labeling workflows. However, manual labeling can be expensive and time consuming, especially when the data is big. It may also require a number of reviews to limit careless errors and other natural approximations when a person spends several hours on the same data set.

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2. Automated Data Labeling (or using tools and techniques to Label Data Automatically)

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This is the fastest and most economical method, but it may be less accurate than manual data labeling, or not accurate at all. It uses learning algorithms and such models to label raw unlabeled data automatically, and can process new unlabeled data to generate training labels. Programmatic labeling is a scalable approach that leverages rules, heuristics, and language models to efficiently create large sets of training labels, allowing for quick adaptation to changing data needs.

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‍Synthetic labeling techniques, such as using GANs, can generate new data or augment existing datasets, but require substantial computational resources. Automated labeling is also widely used for audio recording and audio processing tasks, such as speech recognition and sound classification. The process data steps, including data collection, cleaning, annotation, and quality assurance, are crucial in preparing data for automated labeling by a labeling model.

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This method is especially useful for high quality data and for simple tasks that don’t require human understanding. However, the approximations can be numerous, and atypical or edge cases might not be processed appropriately, especially for images or videos of low quality. It is rare for this method to be self-sufficient in order to obtain quality results. - it is very often associated with human quality reviews (corrections made by a team of Data Labelers).

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‍3. Hybrid Data Labeling

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It is a combination of the two previous methods. It consists of using a human to label some data, while others are automatically labelled. This hybrid approach leverages data annotation by assigning data labeling tasks or labeling tasks to both human annotators and automated systems. Making data labeling efficient involves managing different data points and assigning one or more labels to each data point, ensuring comprehensive coverage and accuracy.

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Data labels and annotated data are important for building robust machine learning models, as they provide the foundation for model training and evaluation. Data labeling works by combining manual and automated approaches, and large language models can assist in automating labeling for tasks like sentiment analysis and entity name recognition. Hybrid labeling is especially valuable for computer vision tasks such as image segmentation, and supports both supervised learning and unsupervised learning methods.

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It is also important to train machine learning models on diverse data sets, including unlabeled data sets, to improve overall performance. This method can be especially useful when the data is of average quality and some tasks are complex while others are simple. It can also include using features from Data Labeling platforms, such as the Active Learning, in order to continuously improve the results of the model and facilitate the work of Data Labelers.

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There is no pre-determined solution to label your data accurately. The best approach is to set aside a few hours to define a labelling strategy. Here is a list of criteria that can be determined in advance of any annotation project:

• Number of Data Labelers required
• Sourcing format (internal, external, profiles with or without functional specialization, etc.)
• Expected functionalities of the labeling platform (Tracking, ergonomics, types of annotation, possible activation of Active Learning functionalities,…)

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💡 It is important to choose the right Data Labeling method: the best method is the one that is adapted to your challenges, to your quality requirements, your resources as well as the nature of the tasks to be performed. Remember that labeling poor quality data can lead to inaccurate and useless results!

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Despite the progress made in recent years, Data Labeling remains a tedious and expensive task for many professionals in the field of Machine Learning. However, it remains essential for training and improving machine learning algorithms, and new solutions are constantly being developed. Remember that a good AI product isn't just about models: to build your products, you will need massive and quality data!