Data labeling, or data annotation, is part of the preprocessing stage when developing a machine learning (ML) model. It requires the identification of raw data (i.e., images, text files, videos), and then the addition of one or more labels to that data to specify its context for the models, allowing the machine learning model 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, 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, then, 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 given project.
Computers use labeled and unlabeled data to train ML models, but what is the difference?
Computers can also use combined data for semi-supervised 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 always easy to implement. As a result, companies must consider multiple factors and methods to determine the best approach to labeling. Since 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 decrease a business’s time to scale, it tends to come at a cost. More accurate data generally improves model predictions, so despite its high cost, the value that it provides is usually well worth the investment. Since data annotation provides more context to datasets, it enhances the performance of exploratory data analysis as well as machine learning (ML) and artificial intelligence (AI) applications. For example, data labeling produces more relevant search results across search engine platforms and better product recommendations on e-commerce platforms. Let’s delve deeper into other key benefits and challenges:
Data labeling provides users, teams and companies with greater context, quality and usability. More specifically, you can expect:
Data labeling is not without its 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:
IBM offers more resources to help transcend data labeling challenges and maximize your overall data labeling experience.
No matter your project size or timeline, IBM Cloud and IBM Watson can enhance your data training processes, expand your data classification efforts, and simplify complex forecasting models.
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