End-to-End ML Model Lifecycle Management on Kubeflow
Data science teams struggled with manual, undocumented model training workflows, no reproducibility across experiments, and no standard path from notebook to production. Model deployment was a high-friction manual process taking days and prone to environment mismatches.
Designed and delivered an enterprise MLOps platform on Kubeflow that automates the full model lifecycle: data ingestion, feature engineering, distributed training, experiment tracking, model registry, automated evaluation, and production serving — all declarative and reproducible.
The platform integrates Kubeflow Pipelines for orchestration, MLflow for experiment tracking and model registry, and KServe for Kubernetes-native model serving with autoscaling and canary deployments.
Kubeflow Pipelines for reproducible, parameterised ML workflows from data prep to model packaging.
MLflow integration for automatic metric, parameter, and artefact logging across all training runs.
Centralised model versioning with stage gates (Staging → Production) and automated evaluation gates.
KServe for Kubernetes-native model serving with canary rollouts, A/B testing, and auto-scaling.