Better ML Demands Better Data Labeling: Key Takeaways

December 24, 2021

Ivan Lee, CEO of Datasaur, and Jai Natarajan, VP of Business Development at iMerit, explain the need of effective data labeling for ML models in an article for Datanami.

Here are 3 key takeaways from the article:

  1. Getting multiple eyeballs on a given piece of text (for NLP use cases) or an image (for computer vision use cases) helps to reduce bias. It also provides project management capabilities to clearly spell out labeling guidelines to ensure labeling standards continue to be met over time.
  2. Context is absolutely critical to the data labeling process. That may be because machines are so lousy at deciphering context. Or maybe it’s because AI use cases are constantly changing. Whatever the cause, the need is clear.
  3. Being meticulous about the data labeling process is important for improving the quality of data, which has a direct impact on the quality of the predictions made by the machine learning models. It can make the difference between having predictions that are accurate 60% to 70% of the time, and getting into that 95% range.

Read the complete article here: Better Machine Learning Demands Better Data Labeling