Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

arXiv:2310.04621v2 Announce Type: replace-cross
Abstract: On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices. However, today's large ML models must be drastically compressed to run efficiently on-device, a hurtle that requires deep, yet currently niche expertise. To engage the broader human-centered ML community in on-device ML experiences, we present the results from an interview study with 30 experts at Apple that specialize in producing …

abstract arxiv compression computation cs.ai cs.hc cs.lg devices however intelligent lessons learned machine machine learning ml models moving practice privacy type