What is Data Labeling for ML
Feeding raw data into a machine learning algorithm rarely generates acceptable results. That’s why ML developers spend significant time preparing, cleansing, and labeling data for use in model training.
Data labeling for machine learning, while time-consuming, is essential for directing the model to make predictions. Labels include tags, annotations, categories, and transcriptions among others. The goal of labels is to pinpoint specific features of the data, including the properties and characteristics of individual data points, to help the model predict its target variable with more accuracy.
For example, the artificial intelligence for self-driving vehicles uses computer vision and labels to signify stop signs, cars, and other features of the road. Online marketing algorithms have scanned consumer reviews and labeled them based on their social sentiment.
What Are the Challenges To Labeling Data For Machine Learning?
The issue with labels is that they’re occasionally subjective, and part of maintaining a productive labeling workforce is ensuring consistency in how employees choose labels. And with large data sets, checking for accuracy is just as important so that labels reflect the real world.
The quality of data labeling can depend on multiple factors:
- Expertise– Some use cases for machine learning require subject matter experts to contextualize data points correctly. A simple example, going back to the social sentiment AI earlier, is recognizing irony, sarcasm, and changes in tone that can greatly impact whether a review is positive or negative. Domain knowledge, or a fundamental understanding of how the data fits into the surrounding industry, is also a nice-to-have, especially for fields like medicine and law that rely on technical terms and jargon.
- Adaptability– Raw data doesn’t stay static. It can change in nature and statistical distribution, and its relationship to a machine learning model’s target variable can also change over time. You need labelers who can adapt to changing volumes and types of raw data.
- Collaboration– Data labeling should not happen in a vacuum. Give your labelers context into how the data fits into your organizational workflow by facilitating communication between your teams and data labelers.
How labeling teams work on the raw data can also impact label quality. Some provide quality assurance by checking a random sample of completed labels and verifying their correctness. Others provide the same data set to multiple teams and check whether there are any discrepancies.
How Do Businesses Undergo Machine Learning Labeling Efficiently?
Labeling is unfortunately labor-intensive. Many machine learning models run on Human-in-the-loop, a supervised training procedure that calls for human intervention to help train and test the model.
To help label data points, companies can turn to several sources:
- Internal employees
- Independent contractors, such as freelancers or temporary workers
- Service providers that specialize in data labeling
Maintaining a data labeling team can be just as strenuous as the task itself. Companies need to train and manage data labelers, provide quality assurance for labels, plan out projects, and find ways to track success.
Scaling the Process with Data Labeling Services For Machine Learning
Development of artificial intelligence models takes a significant amount of data. From the individual frames of a video to comments left on a social media page, going through and applying labels to all those points is a labor-intensive task. That’s why companies have been searching for ways to scale labeling.
One example is fixing incorrect labels in ImageNet, a project for researching visual object recognition software. With over a million training images, the project required a scalable way to identify labeling errors.
DataHeroes contributed with a solution known as Coresets. By strategically choosing a sample subset that reflects the statistical distribution of the entire data set, this approach effectively allows the model to train on a smaller Coreset as opposed to the entire set of data. The strategy also puts additional weight on data points that are at high risk of mislabeling.