In-house vs crowdsourced data labeling: pros, cons, and use cases

If you’re here, it means you’re probably interested in data labelling and potentially crowdsourcing. This guide will be particularly helpful to you if you:

And if you are faced with or have faced one or more of these problems:

• Collecting data for your ML projects feels like swimming against the current, while public data sets offer little to no help.
• You currently have a ton of unlabeled data on your hands, and you need to organize and label it before proceeding with ML training.
• Your data labelling techniques are currently expensive or ineffective.
• You can’t find answers anywhere and don’t know who to turn to.
• You’d like to structure and scale your data labelling processes.
• The labelled data you possess is of substandard quality.
• Quality control remains a big issue.

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