ml-ops-engineer

To deploy ML models into production, you need automated CI/CD pipelines, monitoring infrastructure, and feature store integration. This enables reliable delivery, observability, and automated retraining for production systems.

MLOps infrastructure requires containerized runtimes and orchestration platforms like Kubernetes. You also need model registries and feature stores to ensure reproducible research and reliable delivery across data, training, and serving environments.

Yes, Kubernetes is explicitly supported as the orchestration platform for operationalizing ML systems. It manages containerized runtimes for deployment, monitoring, and infrastructure automation across your pipelines.

Production ML monitoring requires observability tools integrated with your deployment infrastructure. You configure alerts within automated CI/CD pipelines to track model performance and trigger automated retraining when issues arise.

Yes, deploying a fraud-detection model is a core use case. It handles automated retraining, monitoring, and alerting within a Kubernetes environment, ensuring safe and rapid model updates through CI/CD pipelines.

Automated ML deployment relies on access to containerized runtimes and orchestration platforms. Without established Kubernetes infrastructure and model registries, implementing end-to-end governance and observability becomes significantly harder.