Edge & Constrained Deployment
What was the Problem?
Many AI models require powerful cloud servers to run, causing latency issues, high API costs, and privacy concerns for sensitive data.
How I Solved It
Specialized in shrinking and deploying ML models directly onto embedded and mobile hardware so they run locally.
Effort Reduced
Eliminated the need for complex cloud infrastructure maintenance and removed network latency from the user experience.
Profit / Impact Achieved
Reduced server hosting costs by 100% for specific features by relying entirely on on-device computation.
Difficulties Faced
Models lose accuracy when reduced in size (quantization) to fit on edge devices.
How I Overcame It
Used post-training quantization and knowledge distillation to shrink model sizes by 4x while maintaining 98% of their original accuracy.