How to set up an ML Data Labeling Pipeline: Best Practices and Examples

Supervised Machine Learning projects typically require labeled data to train the algorithms. You want to use high-quality data that corresponds to the problem you are trying to solve. But how do you obtain this kind of data? In this session, Magdalena Konkiewicz shows you how to build data labeling pipelines through crowdsourcing. Crowdsourcing is a scalable approach that can be applied to a variety of domains. Magda will share some examples of real-life labeling projects and show you what best practices to apply in the process.

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