Recently, my team has been tackling Machine Learning projects with a unique challenge: no available training data. This situation has pushed our Data Science team into hand-labeling data —a task often viewed as tedious and unexciting. However, I’ve come to embrace this role for several compelling reasons.
Firstly, my schedule is typically packed with back-to-back meetings, leaving me with brief 15 to 30-minute intervals in between. Rather than letting these moments slip away, I’ve found a productive use for them. While I sometimes use this time to jot down notes from the previous meeting or prepare for the next one, I often find myself turning to hand-labeling data. It’s a task that doesn’t demand intense mental effort, allowing me to decompress and clear my headspace.
Another significant motivation is my desire to maximize my team’s time and energy. Their focus should be laser-sharp on solving the project’s core challenges. By taking on tasks like data labeling, I can help remove obstacles and lighten their workload—a trade-off I consider well worth my time. In the early stages of projects, we typically avoid involving external teams for data labeling. Instead, we prefer to create a small sample of high-quality data to validate the project or build a robust evaluation dataset before relying on other labellers.
However, the most compelling reason that draws me to hand-labeling data is the wealth of knowledge it provides. This hands-on experience offers invaluable insights into the complexity of the problem from a human perspective. Having led numerous Data Science projects, I’ve developed an intuitive sense of how human difficulty translates into challenges for automated systems.
Moreover, labeling helps uncover those tricky corner cases—the outliers that can make or break your project’s success. When analyzing a project and estimating its potential risks and complexities, it’s easy to overlook these edge cases and focus solely on the average scenario. But remember, the devil is in the details! By annotating data yourself, you gain a clear understanding of how ambiguous these corner cases can be, especially when they overlap between different classes, and what types of challenges you might encounter in real-world production environments.
Now, let’s address another aspect: convincing your team about the importance of hand-labeling for a few hours. While opinions may vary, seeing you dive headfirst into the task is likely to inspire them to follow suit. The question then arises: how many instances should you label before delegating the task to a labeling team? There’s no one-size-fits-all answer, as it largely depends on the problem at hand. My rule of thumb is to label until the task starts feeling monotonous. For instance, in a recent project involving a text classifier, I tagged over 10,000 instances but discovered that I stopped gaining new insights after about 7,000 text bodies.
So, the next time you or your Data Science team embarks on a new project, don’t shy away from getting your hands dirty with data labeling. Embrace the process and reap the benefits of firsthand experience. Once you’ve gained a clear view of the intricacies of the problem, you can then transition to more scalable approaches. For larger datasets, consider leveraging techniques such as semi-supervised learning, transfer learning, or other programmatic labeling methods. These advanced techniques can help you efficiently label extensive batches of data, building upon the insights you’ve gathered through manual labeling. This hybrid approach combines the deep understanding gained from hands-on experience with the efficiency of automated processes, setting a solid foundation for your machine learning project’s success.
P.S. Here’s a personal tip to boost your labeling productivity: Try listening to Minor Threat or any other classic punk bands while you work. It might just give you the extra energy you need to power through!