Building AI products

Building successful AI products requires moving beyond just training a model to focus on solving real-world problems through data-driven, iterative development, careful ethical planning, and seamless integration into user workflows. The AI product lifecycle typically involves four main stages: Identify (market research), Build (data collection & training), Launch (testing & deployment), and Improve (monitoring & iterating)