Best CI/CD Pipelines for Containerized AI Development

Containerized AI applications combine Docker and Kubernetes with MLOps practices, but their large model artifacts, GPU scheduling requirements, scaling behavior, and separation of batch training from continuously served inference make them more complex than conventional services. Traditional CI/CD systems such as Jenkins, GitLab CI/CD, and GitHub Actions remain useful for building, testing, and deploying containers and infrastructure, yet they are slower and less suitable for frequent changes to prompts, model choices, inference parameters, and provider routing. The proposed approach separates code and infrastructure releases from dynamic application configuration through feature-management tooling such as LaunchDarkly AgentControl Configs, allowing running services to receive prompt, parameter, and model updates without redeployment while preserving version histories and audit trails. It also supports progressive percentage rollouts, AI-specific quality monitoring for measures such as relevance, hallucinations, costs, and latency, and automated rollback when evaluation scores decline. To reduce dependence on individual model vendors, the architecture can abstract multiple providers and dynamically fail over among primary, cloud backup, or local options in response to outages, rate limits, compliance needs, or regional performance conditions. The overall framework emphasizes controlled experimentation and production measurement to balance response quality, cost, latency, and user satisfaction while retaining established CI/CD workflows for substantive application and infrastructure changes.

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