🏷 Data Labeling for ML, part 2

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Labeling methods are becoming increasingly important to the success of machine learning (ML). With up to half the time on an AI project spent dealing with data, the success or failure of these endeavors rely on labeling methods. Our recent survey revealed that 79% of respondents with data labeling experience use the in-house approach even being aware of certain drawbacks of this method. Are there better data labeling approaches and tools to use in ML solutions today?

Soon we will share more on the topic. In the meantime, we would like to check a few things with you via a very simple survey. It will take about 2-3 minutes.

The survey invites all readers of TheSequence. 

As a thank you, we will send you an ebook on the pros, cons, and use cases of two popular data labeling approaches.