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Modern AI applications increasingly rely on cloud-based infrastructure to handle large-scale data processing and model serving. Platforms such as Kubernetes and container-based architectures make it easier to deploy, manage, and scale AI services dynamically based on demand. By integrating CI/CD pipelines, teams can automate model updates and ensure continuous delivery of improvements. Furthermore, the use of monitoring tools and logging systems helps maintain reliability and quickly diagnose issues, making cloud-native AI systems more robust and adaptable to real-world use cases.