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KFP v2 Artifact Orchestration Kubeflow Pipelines (KFP) v2 utilizes a functional DAG model where components execute as isolated containers. Data exchange is managed by persisting output artifacts to an external object store, such as MinIO, which are then referenced as inputs for downstream components. Performance Constraints When handling large datasets betwe
Kubeflow Pipelines v2 reuses task outputs when a task's component signature and inputs match a previous successful execution. This works well until a component's container image is rebuilt under the same tag: the cache key does not appear to change, so stale outputs can be silently reused even though the underlying code changed. The deployment in question us
Kubeflow Pipelines and KServe: Zero-Downtime Model Migration Boundary Kubeflow Pipelines compiles workflow definitions into Argo Workflow YAML and executes PipelineRuns as immutable instances. KServe manages InferenceService deployments and relies on Istio for canary rollouts and traffic splitting. The integration boundary is the handoff from a completed tra
kubeflow
kubernetes