Human-in-the-loop AI (HITL) offers a collaborative approach to machine learning that addresses the limitations of traditional development methods, which often suffer from slow iteration cycles and poor real-world performance. By integrating human input at various stages, HITL enables faster deployment of working models with minimal data, ensuring quality predictions. It consists of two main modes: offline, where humans assist in model training through data annotation, and online, where humans help refine predictions. This approach is akin to agile software development, with active learning reducing data annotation requirements and improving model accuracy. HITL deployment further enhances system reliability by allowing human intervention when models are uncertain, balancing speed, quality, and cost. Despite its advantages, HITL requires significant setup and careful management to avoid biases and maximize effectiveness. Platforms like Humanloop facilitate HITL processes, offering tools for active learning and worker-in-the-loop deployments, drawing on expertise from leading AI companies.