Flood Analytics Information System (FAIS) is a data analytics application funded by the National Science Foundation to integrate crowd intelligence, machine learning, and natural language processing of tweets for flood situational awareness. This national scale prototype combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. FAIS can help users to (i) collect georeferenced tweets, traffic and the USGS webcam images in real time to identify at-risk locations, (ii) detect label and score the objects in time-lapse flooded and non-flooded images, and (iii) perform flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. FAIS is successfully tested in real-time during several hurricane driven flooding events across the south and southeast US where the storms made extensive disruption to critical infrastructure and communities. FAIS is freely accessible to everyone, but primarily designed for real-time and operational use. FAIS video tutorial and educational material are freely available to researchers, students, and professionals via Hydrosystem and Hydroinformatics Research (HHR) group at Clemson University.