Debunking the Common Misconceptions About Data Labeling Work

So you’ve decided to sign up as a SupaAgent.

Once you’ve been approved to join the SupaAgent team, it’s time for you to start working!

If you’re interested in learning how to be a successful SupaAgent. We’ve created this quick guide to help you debunk the myths surrounding data labeling work and what it takes to get high-quality work done! And most importantly, the importance of good quality work.

What Is Data Labeling?

The advancement of Artificial Intelligence (AI) has prompted the need for training data. Machine Learning algorithms rely on a massive amount of training data. Currently, most data isn’t in a form that’s labeled, which is where you come in.

When feeding data to machines to teach them, the data has to be annotated to show the target. The target is what allows AI models the ability to learn from these labeled data and to ultimately —predict.

Upon working on your first project, you will be responsible for a variety of data labeling tasks, which include image annotation, tagging, classification, or transcription. 

Common Misconceptions About Data Labeling

As important as data labeling is in contributing to the accuracy of AI & Machine Learning models, we’ve found that there are a couple of common misconceptions that surround these microtasks.

Let’s take a closer look at the common misconceptions about data labeling and debunk these rumors!

Data Labeling Work Doesn’t Require Accuracy

In data labeling work, you can measure how the data that’s been submitted is consistent with conditions that you’ll find in the real world. Whether you’re working with data labeling for AI that’s for natural language processing, or if it’s for computer vision models, you have to ensure that the data that you’re entering is accurate. The accuracy of the training data is related to the accuracy of the ML model.

It’s Easy Work, anyone can do it

Another common misconception about the nature of data labeling like tasks is people assume it’s work that everyone can do; however, it isn’t always the case. While some data labeling task may look simple in a glance, the meticulousness and attention to detail that is required is not known unless you begin working on the task. Our SupaAgents are a curated pool of individuals who have undergone assessments and even project-specific assessments before they can take on a project, as different projects have varying requirements of skills.

The Importance of Good Quality Work 

The consequence of submitting low-quality data labeling work that hasn’t been carefully checked over has the potential to backfire twice.

Not only will it first impact the AI during model training, but it could also potentially impact any future decisions that your model makes after consuming the data you’ve fed it. For you to ensure that you’re working towards supporting a high-performing AI technology, you need to make sure that you’re entering reliable and trustworthy data.

When you’re doing data labeling work, one of the most critical aspects of your work will be submitting high-quality work. As you’re completing your tasks, you have to ensure that all of your labels are consistent across all of the data sets that you have and that they’re relevant.

Are you interested in learning more about what it takes to be a SupaAgent? Click here to learn more. Signup to be a SupaAgent today!


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