AutoML, or Automated Machine Learning, supports active learning by streamlining the process of selecting the most informative data samples for training machine learning models. Active learning is a method where the model selectively queries the most uncertain data points it encounters, allowing it to learn more efficiently from fewer labeled examples. AutoML frameworks can integrate active learning techniques to automate this selection and labeling process, which helps developers rapidly improve their models without needing extensive manual intervention.
For instance, when a model trained using AutoML begins providing predictions on unlabeled data, it can identify cases where it is uncertain about its predictions. In such scenarios, active learning strategies enable the model to request labels for these uncertain instances. AutoML tools can automate the iterative cycle of training and querying by pre-defining parameters like the query strategy (e.g., uncertainty sampling) and the budget for labeling. This minimizes the data labeling burden on developers, allowing teams to focus on other critical tasks while still enhancing model accuracy over time.
An example of this in practice can be seen in a sentiment analysis application. If the AutoML system encounters new customer reviews it has not seen before, it can flag those reviews with low confidence scores. Developers can then review these flagged items for labeling, leading to a targeted approach to data curation. This process not only reduces the time spent labeling data but also improves the quality of the model by concentrating on the most challenging examples, effectively transforming the routine supervised learning workflow into a more efficient iteration of model improvement.