Why build your own vector DB? To process 25,000 images per second

Future of AI in Security

The podcast discusses the increasing role of AI in video security, particularly focusing on Verkata's solutions which emphasize privacy and safety. Verkata's mission revolves around protecting people and places through advanced cloud-based security systems, serving a diverse range of customers, including gyms, schools, hospitals, and retail stores. The company utilizes over a million cameras globally, leveraging capabilities like object detection to minimize data transmission and provide rapid analytical insights. As AI technologies evolve, there is a strong emphasis on creating more intelligent security cameras that can recognize threats in real time, paving the way for a more proactive approach to safety.

Image Processing and AI Search

The discussion highlights Verkata's impressive image processing capabilities, which can handle around 30,000 images per second, thanks to advanced algorithms and hardware optimizations. The AI-powered search feature enables customers to conduct natural language queries to find specific visual data, such as identifying a person by clothing attributes like color or style. Utilizing the CLIP model, which understands images through their relationship with text, allows for sophisticated searches despite the constraints of processing static images rather than video data. The search system integrates precise object detection that narrows down results by focusing on relevant cropped images, enhancing the efficiency of finding specific subjects within a vast dataset.

Building a Private Vector Database

Privacy and scalability drive Verkata's choice to develop its in-house vector database, ensuring that sensitive customer data remains secure and under the company's control. The custom database was designed to be write-intensive, accommodating the vast influx of image data while maintaining quick response times for search queries. Unique to Verkata, the implementation balances cost, efficiency, and performance, as they avoid third-party solutions to respect their stringent privacy standards. By leveraging technologies like NumPy and caching strategies, the database is optimized for both indexing and retrieval, delivering accurate results to clients during investigative searches.

Challenges and Privacy Considerations

The conversation touches on challenges with AI models in security, notably ensuring the systems do not compromise user privacy while providing accurate results. Verkata implements strict measures, including customizable analytics features that clients can enable or disable based on their preferences, and uses advanced encryption to safeguard search results. Edge cases, where AI struggles with ambiguities—such as distinguishing between adults and children based on visual data—highlight the complexities involved in object detection and querying. Future work involves fine-tuning capabilities to handle intricate queries and improve object detection performance, reinforcing the importance of user-centric design in security technology.

The Stack Overflow Podcast

Why build your own vector DB? To process 25,000 images per second

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Future of AI in Security

Image Processing and AI Search

Building a Private Vector Database

Challenges and Privacy Considerations

Remember Everything You Learn from Podcasts