Continue from this implementation example into live AI market coverage.

Small, specialized AI models trained on high-quality data are being deployed on-device and in regulated environments to provide reliable, low-latency, and privacy-compliant AI capabilities. Examples include voice assistants running locally on phones without network dependency, AI models in hospitals and law firms that keep sensitive data in-house, and real-time object detection in self-driving cars and industrial sensors. Techniques like quantization, pruning, and knowledge distillation enable these smaller models to maintain performance while reducing size and cost. This approach addresses challenges of connectivity, compliance, latency, and cost that large general-purpose models cannot solve effectively.