How to Improve Data Quality with an Efficient Data Labeling Process

In this white paper, we will discuss the significance of data quality in any end-to-end AI project, with a specific focus on the need for data labeling through active learning. Key topics will include:

  • The benefits of active learning, namely the ability to lower the number of labor-related tasks and cost of data labeling necessary for a model to reach the required accuracy
  • Challenges associated with active learning and how to surmount them
  • Use cases that illustrate the massive business opportunity active learning presents and why having labeled data is such a valuable asset


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