WITH AN EFFICIENT DATA LABELING PROCESS
Data needs to be valuable (high quality, labeled, and organized) to drive machine learning model success.
In this white paper, we will discuss the importance of data quality in any end-to-end AI project, with a specific focus on the need for data labeling through active learning.
Read on to discover:
- The benefits of active learning, such as the ability to lower the number of label-related tasks and cost of data labeling necessary for a model to reach the required accuracy
- Challenges associated with active learning and how AI tools and processes can help overcome them
- Use cases of active learning at work and examples that support why labeled data is such a valuable asset