🏷 Data Labeling for ML
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About 45% of the time in data science projects is consumed by processing and labeling data. It’s fair to say that data labeling is one of the most expensive tasks of any machine learning project. How to work with data properly when preparing it? What are the best labeling methods and tools to use in machine learning solutions today?
On July 20, we will send to all our readers an Edge with our overview of a few data labeling platforms. But before that, we would like to check a few things with you via a very simple survey. It will take about 3-5 minutes.
The survey invites machine learning engineers and data scientists, as well as AI enthusiasts. The level of experience is not that relevant.
As a thank you, we will send you a cheat sheet with 40+ useful resources that help you understand and organize data labeling.