Visual data holds immense value but requires advanced management techniques to harness its full potential. Unfortunately, traditional databases are often ill-equipped for the task. But fear not, as vector databases are here to save the day! Thanks to their robust support for embeddings (high-dimensional, dense vectors representing the semantic content of unstructured data) and lightning-fast similarity search capabilities, these specialized databases are designed to handle embeddings generated from unstructured data such as images and videos. In this talk, Frank will dive into the exciting world of vector databases in computer vision applications. He'll explore their advanced indexing and search algorithms that enable accurate and efficient search of visual data and showcase real-world examples in healthcare, retail, finance, and beyond. By the end of this talk, you'll have a deeper understanding of the crucial role vector databases play in unlocking the full potential of visual data - and how they're changing the game in computer vision.