Achieving High-Quality Data with your Data Labeling Guide

Artificial intelligence (AI) and machine learning (ML) applications are booming across various industries, prompting the need for massive amounts of data to train AI and ML solutions. A large part of a model’s accuracy is determined by the data’s: 

  • Quality
  • Accuracy 
  • Relevancy 

While the first step to achieving accuracy with your model is high-quality data, the question is, how do you ensure data quality from the very start? 

Whether you opt for working with a remote, crowdsourced team, or you currently work with an in house labeling team, communication between you and your data labelers is a vital part of getting your data labeled accurately. It may seem obvious but is often a step that many take for granted. 

Data labeling guidelines are often ignored, brushed aside, or overlooked. So it’s not always thought that something as fundamental as a high-quality guideline could pave the way to high-quality data. A well-documented guideline with clear and concise instructions makes all the difference. 


It all boils down to your accuracy rate. While you prep your data labeling team for the proper methods to label your data. Firstly, you would need to prepare a data labeling guide that’s as clear and concise as it possibly can be. In ensuring the quality and accuracy of training data, the rate of error is bound to decline if your labellers know exactly what to do, even in your absence. 

Additionally, it improves data labeling workflows, giving you a more seamless process for your overall process, reducing back and forths between you and your labeling team. The arduous data labeling process can often be laced with edge cases and subjectivity, especially if you’re working with camera captured images for your computer vision model. 

Having a clear data labeling guide can save you from the hassle of wasting hours and money on redoing the work. And most of all, helps your model in achieving a high accuracy rate with the help of human labellers. 

Fortunately, creating a comprehensive data labeling guideline isn’t rocket science.

At Supahands, managing a fully remote workforce lies in our DNA (We’ve been at it since 2014!).

We understand how frustrating it can be when you have to deal with tens of thousands of badly labeled data, which often results in a waste of time and resources, if not done correctly. 

Whether you’re working with a fully remote team or an in house labeling team, we’d like to share our secret sauce with you! 

E-book Guide:

Creating a Successful Labeling Guide for Quality Training Data


Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

You May Also Like