'4 Reasons Why you Need to Outsource your Data Annotation Project' bigdataanalytics dataanalysis
Developing an AI model is expensive, right? For many AI Development companies, the very idea of developing a simple AI model might lead them to assume that they would need millions of dollars to develop it. They also often turn out to be true. However, all the costs you incur should bring you significant profit, and that is the only way to know that you have invested wisely in something.
Such decisions force managers to incur losses for various reasons, including lack of suitable data sets or touchpoints for data generation. Relevant data are absent, a large amount of unstructured and uncleaned data, overhead to train team members to annotate data, rent or purchase annotation software, and more.
Only professional data loggers can keep up with the dynamic demands and consistently deliver the required data sets. At this point, you also need to remember that dataset delivery is not the key, but machine-fed dataset delivery is.An organization is stuck in tunnel vision if you think about it. Bound by protocols, processes, workflows, methodologies, ideologies, work culture, and more, each employee or team member could have more or less an overlapping belief.
When you have internal sources to generate data sets, you are most likely building data sets that are irrelevant, incorrect, or incomplete. Your internal data touchpoints are evolving, and basing training data preparation on such entities could only weaken your AI model.
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