Ttraining models are essential when implementing artificial intelligence in embedded system. These systems are designed to operate with minimal resources, often in real-time and within constrained environments such as IoT devices, automotive systems, robotics, and industrial automation. AI allows embedded systems to make intelligent decisions based on data, and this intelligence stems from well-trained machine learning or deep learning models.

Training models involves feeding data into algorithms to help the system recognize patterns, make predictions, or automate decisions. While the model training is typically done offline on powerful machines, the trained model is later deployed into the embedded system for inference. For instance, in an embedded vision system, a trained model can help detect objects or gestures without needing cloud-based analysis.

Without training models, the AI component would lack the capability to improve or adapt, severely limiting its potential. Even lightweight models like TinyML are pre-trained to suit embedded platforms. Hence, training is a foundational step in building efficient and accurate AI-powered embedded systems.

To start building such smart systems, consider enrolling in a best artificial intelligence course that includes model development and deployment.