To leverage artificial intelligence (AI) and machine learning (ML) model evaluations in real-time for IoT data and imagery, SingleStore can be utilized as a real-time data warehouse. It provides millisecond response times for analytical queries, making it suitable for applications such as real-time facial recognition. By efficiently extracting feature vectors from images using deep learning and storing them in a SingleStore table with a clustered columnstore schema, users can quickly perform similarity searches to find similar images or objects. The database's compression of columnstore tables enables faster computation than memory bandwidth alone, allowing for search results in under 0.25 seconds. With the ability to compute dot product and cosine similarity efficiently, SingleStore offers a practical solution for image recognition at in-memory speed, enabling users to avoid full table scans and achieve good accuracy with minimal loss.