Data labeling is one of the major pain points of businesses incorporating AI solutions is data annotation. Data labeling or data annotation is never a one-off event. It is a continuous process. Data is essential, but it should be relevant to your project goals. The data annotation tools market size was over $1 billion in 2010 and this is expected to grow at more than 30% CAGR by 2020.
But, where does the AI’s promise of exploiting new opportunities go wrong? Sometimes during the data labeling process. Deep learning models demand thousands of data pieces for the model to perform reasonably well. For example, when training an AI-based robotic arm to maneuver complex machinery, every slight variation in the job could require another batch of training data set. But, gathering such data can be expensive and sometimes downright impossible, and difficult to annotate for any business.
One major issue is managing a vast workforce that can manually process sizable unstructured data sets. The next is maintaining high-quality standards across the workforce. Many issues might crop during data annotation projects.
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