Walk through the lifecycle of an AI project from ideation to deployment

Focus on clearly outlining each stage of the AI project lifecycle, starting from ideation. Discuss how you would identify a problem that AI can solve, including the importance of user research and defining success metrics. Move on to data collection and preparation, emphasizing the significance of data quality and ethical considerations. Explain the model development phase, including algorithm selection and training, and touch on validation and testing processes. Finally, detail the deployment phase, including integration into existing systems, monitoring performance, and gathering user feedback for continuous improvement. Be prepared to address potential challenges at each stage and how you would mitigate them.