On-Device AI and Personalization
Federated learning trains models across devices without centralizing raw data, enabling personal recommendations while protecting privacy. Developers balance update cadence, battery impact, and drift. Have you tried it in production? Tell us what worked and subscribe to follow practical case studies.
On-Device AI and Personalization
Quantization, pruning, and distillation shrink models to mobile-friendly sizes while preserving accuracy. Mixed-precision execution and hardware acceleration turn once-impossible features into everyday magic. Share your favorite optimization tricks and help others navigate the trade-offs between speed, size, and quality.