As AI applications move from prototypes to production, containerization and orchestration become critical. This post outlines the path from Docker to Kubernetes, emphasizing how to structure AI services for scalability and resilience. Key topics include containerizing AI models, managing stateful workloads, and implementing autoscaling based on metrics like GPU utilization or request latency. The discussion also touches on common pitfalls, such as resource contention and cold starts, and offers architectural patterns to mitigate them. For teams building AI infrastructure, understanding these patterns is essential for delivering reliable, cost-effective services. The post serves as a practical reference for DevOps engineers and platform teams looking to modernize their AI deployment pipelines.
Explore the transition from Docker to Kubernetes for AI applications, covering service architecture and autoscaling strategies for production workloads.