A production MLOps pipeline is what separates a data science experiment from a reliable software feature. On Kubernetes, this means moving from notebook-based ML to a path that automates everything from data validation to model serving. Understanding your MLOps maturity is the first step in this journey.
The Pillars of K8s MLOps
1. Automated Training and Retraining
Use Kubeflow Pipelines or Argo Workflows to automate your training runs. If you're currently relying on simple scripts, see our guide on replacing cron jobs with proper ML pipeline orchestration. These should be triggered by feature drift alerts or a regular schedule.
2. Model Registry and Artifact Management
Store your models in a central registry. Use Terraform to manage the storage and networking required for these artifacts.
3. Reliable Serving and Rollouts
Deploy your models using canary releases or shadow traffic. This ensures that new models are validated against production traffic before they impact your system SLOs.
Final Takeaway
An MLOps pipeline on Kubernetes provides the automation and consistency needed for production AI. By standardizing your release path and integrating deep observability, you enable your team to ship better models, faster.
Need help building or refining your MLOps pipelines on Kubernetes? We help teams design and implement automated training, evaluation, and serving workflows for production ML. Book a free infrastructure audit and we’ll review your MLOps path.
