Introduction
Kubeflow is an open-source platform built on Kubernetes that enables teams to develop, orchestrate, deploy, and manage machine learning systems using containerized, scalable, and reproducible components.
Designed to bring DevOps principles to the ML world (often called MLOps), Kubeflow empowers Cake engineers and data scientists to collaborate across the ML lifecycle—from experimentation to production—while leveraging the full capabilities of Kubernetes.
Key benefits of using Kubeflow within the Cake platform include:
End-to-End ML Lifecycle Management: Supports model development, training, hyperparameter tuning, deployment, monitoring, and retraining—all within a single, unified platform.
Reproducible Pipelines: Enables definition of ML workflows as pipelines using Kubeflow Pipelines, ensuring consistency and traceability across experiments and runs.
Scalable Training and Inference: Leverages Kubernetes-native scaling for distributed training (e.g., with TensorFlow, PyTorch, XGBoost) and auto-scaling model serving using KFServing or KServe.
Built-in Experiment Tracking and Metadata: Automatically tracks artifacts, metrics, versions, and lineage, enabling auditability and iterative development across teams.
Secure, Multi-Tenant Access: Supports namespace-based isolation, role-based access control, and integration with identity providers like Dex and OIDC to ensure compliance and secure access across ML teams.
Kubeflow can be used to operationalize machine learning workloads across the platform—serving use cases such as recommendation systems, real-time fraud detection, content ranking, and internal AI tooling. It integrates tightly with GitOps workflows, CI/CD pipelines, and infrastructure automation tools like Terraform and Argo CD, providing a consistent, scalable MLOps foundation.
Kubeflow ensures that its ML capabilities are scalable, reproducible, and production-ready—enabling data science and engineering teams to collaborate effectively and deliver impactful machine learning solutions with confidence.