Active Learning
at Work

Before running any cutting-edge machine learning algorithms or deploying any models, the data involved needs to be high quality and labeled. However, data collection, including cleaning and wrangling, is a tedious, time consuming, and iterative process that typically involves data labeling and model training.

Active learning is a framework allowing users to reduce the cost of data labeling necessary for a model to reach the required accuracy. It can be used:

• When not all data can be annotated because it is too costly or complicated.
• To speed up the labeling procedure by leveraging previously labeled data.
• To optimize the order in which unlabeled data is processed.