The KFP Operator provides a Kubernetes-native API for Kubeflow Pipelines. Deploy and manage ML pipelines using kubectl, Helm, and GitOps workflows with Custom Resource Definitions.
Kubernetes-Native
Manage ML pipelines as Kubernetes resources using kubectl, Helm, and GitOps workflows.
Event-Driven
Trigger pipeline runs automatically based on schedules, events, or data changes.
Production-Ready
Enterprise security, observability, and integration with existing Kubernetes infrastructure.
How It Works
Define Pipelines as Code
Create Kubernetes manifests for your ML pipelines and version control them with your code.
apiVersion: pipelines.kubeflow.org/v1beta1
kind: Pipeline
metadata:
name: training-pipeline
spec:
image: my-org/ml-pipeline:v1.2.0
env:
- name: MODEL_VERSION
value: "v2.1"
Deploy with kubectl
Use standard Kubernetes tools to deploy and manage your ML workflows.
# Deploy your pipeline
kubectl apply -f pipeline.yaml
# Trigger a run
kubectl apply -f runconfiguration.yaml
# Monitor status
kubectl get mlr,mlrc
Get Started
Installation
helm repo add kfp-operator https://sky-uk.github.io/kfp-operator/
helm install kfp-operator kfp-operator/kfp-operatorOpen Source
100% open source and welcomes contributions. Built by Sky’s ML Platform team and used in production.