The AI lifecycle is the end-to-end process of developing, deploying, and maintaining an AI system. It includes stages such as problem definition, data preparation, model training, evaluation, deployment, monitoring, and ongoing governance to ensure performance, accuracy, and compliance over time.
Why it matters: Managing the full lifecycle ensures AI systems remain accurate, secure, compliant, and aligned with business goals over time.
