The AI Application Lifecycle describes the stages involved in creating, deploying, operating, and improving an AI-powered application. It typically moves from defining a use case and preparing data through model development, testing, deployment, monitoring, and ongoing refinement.
A well-managed lifecycle keeps AI applications reliable as data, user needs, and operating conditions change. It brings together data engineering, model training, software development, deployment infrastructure, performance monitoring, and governance. Common practices include MLOps, continuous evaluation, model versioning, observability, and automated retraining. Related terms include MLOps, Model Deployment, Model Monitoring, AI Governance, and Machine Learning.
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