Active Learning
What is Active Learning?
Active Learning in Edge AI is a machine learning technique where models selectively query the most informative data for labeling. This human-in-the-loop approach helps AI systems learn faster and perform better with less data—making it ideal for resource-constrained edge devices where efficiency and adaptability matter most.
Why is it Used?
Active Learning is used to reduce the time, cost, and data required to train AI models. In Edge AI, it ensures that only the most critical, uncertain, or complex data points are processed or labeled—optimizing both bandwidth and computational power at the edge.
How is it Used?
In Edge AI deployments, Active Learning systems identify ambiguous data captured by edge devices (like sensors or cameras) and send only these samples for human review or cloud processing. This iterative feedback loop improves model accuracy while keeping training local and efficient.
Types of Active Learning
Uncertainty Sampling: The model queries data points it’s least confident about.
Query by Committee: Multiple models vote on predictions; disagreements indicate useful samples.
Expected Model Change: Prioritizes data that would most impact model parameters.
Benefits of Active Learning
Reduces labeling costs by focusing on key data points.
Improves accuracy and generalization in dynamic environments.
Minimizes cloud dependency, enhancing data privacy at the edge.
Enables continuous model improvement in real-time scenarios.