In your AI data center, you are responsible for deploying and managing multiple machine learning models in production. To streamline this process, you decide to implement MLOps practices with a focus on job scheduling and orchestration. Which of the following strategies is most aligned with achieving reliable and efficient model deployment?

Using a CI/CD pipeline to automate model training, validation, and deployment (A) is the most

aligned with reliable and efficient MLOps practices. Continuous Integration/Continuous Deployment

(CI/CD) automates the ML lifecycle—building, testing, and deploying models—ensuring consistency,

reducing errors, and enabling rapid iteration. Tools like Kubeflow or Jenkins, integrated with NVIDIA

GPU Operator, schedule jobs efficiently on GPU clusters, validating models in staging environments

before production rollout.

Running all jobs simultaneously(B) risks resource contention and instability, not efficiency.

Manual triggering(C) is slow and error-prone, counter to MLOps automation goals.

Direct deployment without staging(D) skips validation, risking unreliable models in production.

NVIDIA supports CI/CD for AI deployment in its MLOps guidelines (A).

Reference:NVIDIA MLOps Guide; Kubeflow integration on nvidia.com.