How does Machine Learning Work?

Machine learning follows a general process of:

  1. Data collection and preparation: Data is gathered from sensors, logs, or datasets and formatted for analysis.
  2. Model training: Algorithms identify patterns within data to create a predictive or classification model.
  3. Inference and feedback: The model is deployed to make predictions on new data, and results are used to refine accuracy.

In embedded or on-device systems, inference often runs on specialized processors, such as Arm NPUs or GPUs, to achieve low-latency, power-efficient performance.

Key Types of Machine Learning

Type Description Example in Arm-based systems
Supervised learning Models learn from labeled data (known inputs and outputs). Image classification in smartphone cameras.
Unsupervised learning Models find patterns in unlabeled data. Detecting anomalies in IoT sensor data.
Reinforcement learning Models learn by trial and error, guided by rewards. Adaptive control for autonomous vehicles.

Related Solutions and Resources