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