Why build your own vector DB? To process 25,000 images per second
Ben and Ryan discuss with Babak Behzad the efficient vectorization of images into a database, exploring technical factors, processing locations, and privacy concerns in image recognition.
MAIN POINTS
- Verkada's pipeline vectorizes 25,000 images per second into a custom vector database.
- Discussion on whether speed is attributed to technical expertise or hardware capabilities.
- Comparison between on-device and off-device image processing benefits.
- Emphasis on privacy importance in video camera image recognition.
TAKEAWAYS
- Efficient image vectorization requires a balance of technical skill and robust hardware.
- On-device processing can offer advantages in speed and privacy.
- Off-device processing may provide more computational power and flexibility.
- Privacy is a critical consideration in implementing image recognition technologies.