The quality of a machine learning project comes down to how you handle three important factors: data collection, data preprocessing, and data labeling. Data labeling is integral because it’s literally labeling the data that will teach your model to learn its task.
However, data labeling is often time consuming and complex. For example, image recognition systems often require bounding boxes drawn around specific objects, while product recommendation and sentiment analysis systems can require complex cultural knowledge for accurate data labeling. And don’t forget that a dataset could contain tens of thousands of samples in need of labeling, if not more.
With this in mind, selecting the right approach to data labeling for a machine learning project means taking into account the complexity of the task, the size of the project, and your project timeline. With these factors in mind, we’ve listed five common approaches to data labeling along with pros and cons for each.
Data Labeling for Machine Learning
Data labeling for machine learning can be broadly classified into the categories listed below:
In-house: As the name implies, this is when your data labelers are your in-house team of data scientists. This approach has a number of immediate benefits: tracking progress is simple, and accuracy and quality levels are reliable. However, outside of big companies with internal data science teams, in-house data labeling may not be a viable option.
Outsourcing: Outsourcing is a good option for creating a team to label a project over a set period of time. By advertising your project through job sites or your company’s social media, you can create a funnel for potential applicants. From there, an interviewing and testing process will ensure that only those with the appropriate skill set make it onto your labeling team. This is a great way to build a temporary team, but it also requires a certain amount of planning and organization; your new staff will need training to become adept at their new job and complete it to your specifications. Furthermore, if you don’t already have one, you might also need to license a data labeling tool for your team to work on.
Crowdsourcing: Crowdsourcing platforms are a way to enlist help from people across the globe to work on particular tasks. Because crowdsourcing jobs can be picked up from anywhere in the world and performed as tasks become available, it is extremely quick and cost effective. However, crowdsourcing platforms can vary wildly in terms of worker quality, quality assurance, and tools for project and worker management. Therefore, it’s important to be aware of how the platform approaches these factors when looking at crowdsourcing options.
Synthetic: Synthetic labeling is the creation or generation of new data that contains the attributes necessary for your project. One way to perform synthetic labeling is through generative adversarial networks (GANs). A GAN utilizes two neural networks (a generator and a discriminator) which compete to create fake data and distinguish between real and fake data respectively. This results in highly realistic new data. GANs and other synthetic labeling methods allow you to create all-new data from pre-existing datasets. This makes them time effective and excellent at producing high quality data. However, at present, synthetic labeling methods require large amounts of computing power, which can make them very expensive.
Programmed: Programmatic data labeling is the process of using scripts to automatically label data. This process can automate tasks including image and text annotation, which eliminates the need for large numbers of human labelers. A computer program also does not need rest, so you can expect results much faster than when working with humans. However, automated data labeling is still far from perfect. Programmatic data labeling is therefore often combined with a dedicated quality assurance team. This team reviews the dataset as it is being labeled.
We’ve summarized the data labeling approaches in the table below for easy reference:
Each different approach to data labeling has its own strengths and weaknesses. Knowing which approach is best for you depends on a number of factors. These can include the complexity of your use case, the training data, the size of your company and data science team, your finances, and your deadline. Be sure to keep these in mind when considering data labeling solutions. Be sure to check out our dedicated guide to training data for a more detailed look at data for machine learning.
If you still aren’t sure about the best data labeling approach for your particular machine learning project, please get in touch with our team. Lionbridge provides data services for machine learning to tech companies across the world, in a variety of different fields. With access to a community of 1,000,000+ contributors, Lionbridge has the experience and expertise to help you define, create, and label the data you need for your project.