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Data labelling: In-house or outsourcing?

December 20, 2021
in News & Updates
Data Labelling Challenges

In-house: deploying existing data scientists and resources. You have more control over the results which can beef up accuracy and increase quality. However, this approach is time-consuming and expensive, especially if annotators are hired and trained from scratch. Thus, in-housing favors large companies with extensive resources.

Outsourcing: hiring managed teams with pre-vetted staff and pre-built labelling tools. You have less control over workflows, but this approach is considered an optimum choice for high-level periodic projects. Furthermore, outsourcing platforms can also commit knowledge of the industries as they serve several customers.

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