Data labeling, or data annotation, is part of the preprocessing stage when developing a machine learning (ML) model.
Data labeling involves identifying raw data, such as images, text files or videos and assigning one or more labels to specify its context for machine learning models. These labels help the models interpret the data correctly, enabling them to make accurate predictions.
Data labeling underpins different machine learning and deep learning use cases, including computer vision and natural language processing (NLP).
Companies integrate software, processes and data annotators to clean, structure and label data. This training data becomes the foundation for machine learning models. These labels allow analysts to isolate variables within datasets, and this process, in turn, enables the selection of optimal data predictors for ML models. The labels identify the appropriate data vectors to be pulled in for model training, where the model learns to make the best predictions.
Along with machine assistance, data labeling tasks require “human-in-the-loop (HITL)” participation. HITL leverages the judgment of human “data labelers” toward creating, training, fine-tuning and testing ML models. They help guide the data labeling process by feeding the models datasets that are most applicable to a project.
Computers use labeled and unlabeled data to train ML models, but what is the difference?
Computers can also use combined data for semisupervised learning, which reduces the need for manually labeled data while providing a large annotated dataset.
Data labeling is a critical step in developing a high-performance ML model. Though labeling appears simple, it’s not necessarily easy to implement. As a result, companies must consider multiple factors and methods to determine the best approach to labeling. As each data labeling method has its pros and cons, a detailed assessment of task complexity, as well as the size, scope and duration of the project is advised.
Here are some paths to labeling your data:
The general tradeoff of data labeling is that, while it can accelerate a business’s scaling process, it often comes at a significant cost. More accurate data leads to better model predictions, making data labeling a valuable but expensive investment. Despite its high cost, businesses find it worthwhile due to the enhanced accuracy it provides.
Because data annotation adds more context to datasets, it improves the performance of exploratory data analysis, machine learning (ML), and artificial intelligence (AI) applications. For instance, labeled data contributes to more relevant search results on search engine platforms and better product recommendations in e-commerce. Let’s now explore other key benefits and challenges in more detail.
Data labeling provides users, teams and companies with greater context, quality and usability. More specifically, you can expect:
Data labeling comes with its own set of challenges. In particular, some of the most common challenges are:
No matter the approach, the following best practices optimize data labeling accuracy and efficiency:
Though data labeling can enhance accuracy, quality and usability in multiple contexts across industries, its more prominent use cases include:
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