Building AI Products that People Love

We all want more AI in our web products, and we want AI to do things past traditional AI problems like search optimization and fraud detection. But creating these products outside of well researched spaces is not clear. It’s easy in these spaces, for ML practitioners to work hard building incredibly accurate models only to release them in production and find out that nobody likes using the final product using their algorithm because their model wasn’t designed for the user. In this session, we’ll use a case study to discuss the problems and pitfalls how to iteratively build AI products with an aggressive focus on end user usability. Attendees will leave understanding strategies in road-mapping, simulation, and validation that will help bring the voice of their product to their AI/ML product development cycle.