MLOps for Large-scale AI

Why?

• Managing models, experiments, datasets, and deployments at AI scale requires mature MLOps practices and tools.
• Automation for training, validation, deployment, rollback, and monitoring is essential to reduce operational risk.

What?

• Standardize on platforms and pipelines for experiment tracking, reproducible training, and production deployment (Kubeflow, Flyte, MLflow, Argo).
• Implement policies for model validation, AB testing, and staged rollouts with rollback capabilities.
• Integrate cost, performance, and carbon metrics into MLOps dashboards.