Supervised Learning
What is Supervised Learning?
Supervised learning is a type of machine learning where Edge AI systems are trained on labeled data to predict outcomes or classify information accurately. Also known as guided learning, it enables AI models to make precise, data-driven decisions directly on edge devices without cloud dependency.
Why Is It Used?
Supervised learning is used in Edge AI to enable real-time, intelligent decision-making for tasks such as anomaly detection, predictive maintenance, and image recognition. It ensures AI systems perform reliably in latency-sensitive and bandwidth-constrained environments.
How Is It Used?
Collect labeled datasets from sensors, cameras, or IoT devices.
Train models locally or via hybrid edge-cloud frameworks.
Deploy trained models on edge devices for immediate inference.
Continuously update models with new labeled data for improved accuracy.
Types of Supervised Learning
Regression: Predicts continuous values (e.g., temperature, vibration levels).
Classification: Categorizes inputs into predefined classes (e.g., defect detection, object recognition).
Benefits of Supervised Learning
Low Latency: Instant predictions at the edge.
Data Privacy: Sensitive data stays local.
Bandwidth Efficiency: Reduces cloud dependency.
High Accuracy: Models learn from labeled, validated data for precise outcomes.