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Businesses are embracing Machine Learning (ML) and Artificial Intelligence (AI) for text and image annotation because of the accuracy, speed and comprehensiveness these services provide. In addition, these services eliminate the risks associated with managing data diversity, reducing bias and scaling. The annotation process begins with marking up a dataset and its characteristics with a
Both automation and human factors play a crucial role in the success of any data labeling or annotation projects. The groundwork involved in building these projects is time-consuming, complex and expensive. To a large extent, the success of any such projects depends on data scientists, data engineers and data modelers. In fact they are the
Companies that rely on internal teams or automation to annotate data often find it difficult to manage increasing workloads and yet assure the same quality, speed, and security. Automation is the first casualty in such instances. Why? A lot of time is spent on perfecting algorithmic models to accurately match complex behavioral patterns and make