https://www.techtarget.com/whatis/definition/data-labeling
Data labeling is the process of identifying and tagging data samples commonly used in the context of training machine learning (ML) models. The process can be manual, but it's usually performed or assisted by software. Data labeling helps machine learning models make accurate predictions. It's also useful in processes such as computer vision, natural language processing (NLP) and speech recognition.
The process starts with raw data, such as images or text data, that's collected, and then one or more identifying labels are applied to each segment of data to specify the data's context in the ML model. The labels used to identify data features must be informative, specific and independent to produce a quality model.
Data labeling is an important part of data preprocessing for ML, particularly for supervised learning. In supervised learning, a machine learning program is trained on a labeled data set. Models are trained until they can detect the underlying relationship between the input data and the output labels. In this setting, data labeling helps the model process and understand the input data.
For example, a model trained to identify images of animals is provided with multiple images of various types of animals from which it learns the common features of each. This enables the model to correctly identify the animals in unlabeled data.
Data labeling is also used when constructing machine learning algorithms for autonomous vehicles, such as self-driving cars. They need to be able to tell the difference between objects that might get in their way. This information lets these vehicles process the external world and drive safely. Data labeling enables the car's artificial intelligence (AI) to tell the differences between a person and another car or between the street and the sky. Key features of those objects or data points get labeled and similarities between them are identified.
Like supervised ML, computer vision also uses data labeling to help identify raw data. Computer vision deals with how computers interpret digital images and videos.
NLP, which enables a program to understand human language, uses data labeling to extract and organize data from text. Specific text elements are identified and labeled in an NLP model for understanding.
ML and deep learning systems require massive amounts of data to establish a foundation for reliable learning patterns. The data they use to inform active learning must be labeled or annotated based on data features that help the model organize the data into patterns that produce a desired answer.
The data labeling process starts with data collection and then moves on to data tagging and quality assurance (QA). It ends when the model starts training. There are four key steps in the process:
A properly labeled data set provides a ground truth against which the ML model checks its predictions for accuracy and continues refining its algorithm. A high-quality algorithm is high in accuracy, referring to the proximity of certain labels in the data set to the ground truth.
Errors in data labeling impair the quality of the training data set and the performance of any predictive models it's used for. To mitigate this and ensure high performance, many organizations take what's called a human-in-the-loop approach. This is where people are involved in training and testing the data.
Data labeling, classification and annotation are three methods used to prepare data that's fed to ML models. They're also practical for handling large volumes of raw data in need of organizing. However, these three methods are carried out in different ways and for different purposes.
With this approach, a data expert predefines labels and applies them to each data point within a data set. The process provides a model with the necessary context in its training data to learn from that data and produce outputs, such as insights or predictions.
Data classification refers to categorizing data, either categorically or using a binary approach. Successful data classification depends on good data labeling practices. For example, a binary classification task can sort emails, determining if they are spam or not spam, by examining labeled data. Data classification is mainly used in supervised learning when teaching the ML model to make predictions.
Data annotation provides specific information about data inputs, giving ML models a deeper understanding of those inputs. For instance, data annotation can be applied in autonomous vehicle systems where a model must examine many different objects in its surroundings. Annotating different objects with more context and detail enhances the system's understanding of its surroundings.
The different types of data labeling are defined by the medium of the data being labeled:
Benefits that come from data labeling include the following:
A few challenges are evident for businesses tasked with large-scale data labeling:
Best practices to follow in data labeling include the following:
An enterprise can use several methods to structure and label its data. The options range from using in-house staff to crowdsourcing and data labeling services:
There is no singular optimal method for labeling data. Enterprises should use the method or combination of methods that best suits their needs. Some questions must be considered when choosing a high-quality data labeling method, such as the following:
A good data labeling team should ideally have domain knowledge of the industry an enterprise serves. Human labelers who have outside context guiding them are more accurate than ones who don't, and they also have diverse perspectives. They must also be flexible and nimble because data labeling and ML are iterative processes, always changing and evolving as more information is taken in.
Spending on AI projects typically goes toward preparing, cleaning and labeling data. Manual data labeling is the most time-consuming and expensive method, but it might be warranted for important applications.
Critics of AI speculate that automation will put low-skill jobs, such as call center work and truck and Uber driving, at risk. It will be easier for machines to perform these types of rote tasks.
However, some experts believe data labeling itself will be a new low-skill job opportunity for people displaced by automation. There will be an ever-growing surplus of data and machines that need to be trained to process that data. Skilled data labelers will be needed to properly label data and train the advanced AI and ML models.
Companies that have incorporated data labeling into their business models include the following:
As advanced technologies, such as AI and ML, become more pervasive across industries for real-world uses, AI-powered data labeling tools are becoming more common. As big data collection becomes more common as well, ML algorithms are fed large training data volumes to learn new information. The algorithms are also expected to become better at categorization or classification. The goal is to efficiently label data volumes so people don't have to do these painstaking tasks.
Other trends include an expansion of crowdsourcing, where workers are pooled to collectively label data. Large data sets benefit from having people with various backgrounds and areas of expertise add detailed information to data points.
Finally, data privacy concerns have grown as AI and ML models are being fed large data sets, often including people's personal data. Therefore, businesses have to be cautious when labeling data to not violate local, state and federal data protection laws and ensure the privacy of data subjects.
Data labeling is a subset of data preprocessing. Learn about various data science tools, including ones used for data preprocessing.
25 Jul 2024