Kubeflow Pipelines to Vertex AI Pipelines Migration Services

Why Vertex AI Pipelines?

Run ML Pipelines Without Managing Kubernetes

Managed Pipeline Orchestration

Run machine learning pipelines without maintaining the Kubeflow Pipelines backend, databases, metadata services, or orchestration infrastructure.

Familiar Kubeflow Development

Continue defining portable machine learning workflows using supported versions of the Kubeflow Pipelines SDK.

Integrated Vertex AI Services

Connect pipeline steps with managed training, Experiments, Model Registry, endpoints, batch prediction, evaluation, and monitoring.

Google Cloud Governance

Apply IAM, service accounts, private networking, encryption, audit logging, and organization policies across pipeline workloads.

How We Migrate

A Structured Kubeflow Pipelines Migration

1

Assess

Review pipeline definitions, SDK versions, components, container images, artifacts, parameters, schedules, secrets, storage, metadata, and Kubernetes dependencies.
2

Design

Define the Vertex AI Pipelines architecture for pipeline roots, service accounts, networking, artifacts, metadata, scheduling, compute, and governance.
3

Adapt Pipelines and Components

Upgrade pipeline code where required and update components, inputs, outputs, container images, resource settings, and cloud integrations.
4

Migrate Pipeline Operations

Rebuild schedules, triggers, permissions, secrets, monitoring, artifact storage, CI/CD, and deployment processes for Google Cloud.
5

Validate and Cut Over

Compare execution order, parameters, artifacts, caching, retries, outputs, lineage, runtime, and failure handling before moving production workflows.

Why Codimite?

End-to-End ML Pipeline Modernization

Codimite combines Google Cloud architecture, Kubeflow engineering, containerization, pipeline development, and MLOps governance expertise.

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Pipeline Compatibility Assessment. We identify reusable KFP code, outdated SDK patterns, unsupported features, Kubernetes dependencies, and components requiring redesign.
Managed Target Architecture. We design secure pipeline execution using Vertex AI, Cloud Storage, Artifact Registry, IAM, networking, and supporting Google Cloud services.
Component Modernization. We adapt lightweight and containerized components for managed execution, portable artifacts, and direct integration with Vertex AI services.
Controlled Workflow Transition. We reduce migration risk through test pipelines, parallel runs, output comparisons, phased scheduling, monitoring, and rollback planning.

Platform Comparison

Kubeflow Pipelines and Vertex AI Pipelines at a Glance

Both platforms support container-based ML workflows, but Vertex AI Pipelines provides managed orchestration and deeper integration with Google Cloud AI services.

Area	Kubeflow Pipelines	Vertex AI Pipelines
Platform approach	Open-source platform for building and running machine learning workflows on Kubernetes	Managed Google Cloud service for orchestrating machine learning workflows
Infrastructure	Teams operate the pipeline backend, databases, metadata services, storage, and Kubernetes infrastructure	Google manages the core pipeline orchestration infrastructure
Pipeline authoring	Uses the Kubeflow Pipelines Python SDK to define and compile workflows	Runs workflows authored with supported Kubeflow Pipelines SDK or TFX versions
Pipeline execution	Components commonly execute as containers or Kubernetes resources within a cluster	Components run through managed Google Cloud pipeline infrastructure
SDK compatibility	Support depends on the installed KFP backend and SDK version	Supports selected SDK versions and compatible pipeline specifications
Legacy pipelines	Existing environments may use older KFP v1 syntax and Argo-based specifications	Legacy pipelines may require conversion to supported KFP v2 patterns
Components	Supports lightweight, containerized, and Kubernetes-aware components	Supports compatible lightweight and containerized pipeline components
Kubernetes resources	Components can directly use ConfigMaps, Secrets, volumes, operators, and custom resources	Kubernetes-specific dependencies must be replaced with supported Google Cloud services
Artifact storage	Commonly uses MinIO, object storage, persistent volumes, or custom repositories	Uses Cloud Storage as the pipeline root for artifacts and outputs
Metadata	Uses ML Metadata services operated within the Kubeflow environment	Integrates pipeline executions and artifacts with managed Vertex ML Metadata
Experiments and runs	Kubeflow UI manages experiments, runs, recurring runs, and pipeline definitions	Google Cloud Console and APIs manage pipeline jobs, schedules, metadata, and visualizations
Scheduling	Recurring runs use Kubeflow Pipelines or external Kubernetes schedulers	Pipeline schedules can be managed through Vertex AI and supporting Google Cloud services
Caching	Supports task caching based on the KFP backend and configuration	Provides managed execution caching for compatible pipeline tasks
Control flow	Supports dependencies, conditions, loops, exit handlers, and selected advanced patterns	Supports compatible KFP control flow, with some unsupported patterns requiring redesign
Resource allocation	Uses Kubernetes CPU, memory, GPU, node selectors, tolerations, and scheduling controls	Uses managed machine types, accelerators, custom training jobs, and Vertex AI configurations
Secrets management	Commonly uses Kubernetes Secrets, ConfigMaps, or external secret systems	Uses Secret Manager, service accounts, IAM, and secure Google Cloud integrations
Container registry	Uses a registry configured for the Kubernetes environment	Uses Artifact Registry for managed storage and access to container images
Training integration	Training runs through Kubernetes jobs, operators, custom containers, or external platforms	Integrates with Vertex AI Custom Training, AutoML, hyperparameter tuning, GPUs, and TPUs
Model registry	Requires Kubeflow Model Registry, MLflow, or another connected service	Integrates directly with Vertex AI Model Registry
Model deployment	Commonly connects to KServe, Kubernetes services, or external deployment platforms	Integrates with Vertex AI endpoints, batch prediction, traffic splitting, and monitoring
Monitoring	Teams configure Kubernetes logging, Prometheus, and other observability tools	Integrates with Cloud Logging, Cloud Monitoring, pipeline visualizations, and Vertex AI services
Security	Depends on Kubernetes RBAC, service accounts, network policies, and platform configuration	Uses Google Cloud IAM, service accounts, private networking, encryption, and audit logs
Scaling	Teams scale nodes, backend services, databases, and execution infrastructure	Google manages orchestration scaling while teams configure workload-specific compute
Operations	Teams manage installation, upgrades, availability, storage, networking, and troubleshooting	Google manages the pipeline service while teams focus on ML workflow logic
Portability	Designed for portability across compatible Kubernetes and KFP environments	Uses portable KFP concepts with managed Google Cloud integrations
Best suited for	Teams requiring Kubernetes-level control and cross-platform pipeline deployment	Teams seeking managed ML orchestration and integrated MLOps on Google Cloud

FAQs

Kubeflow Pipelines to Vertex AI Pipelines Migration FAQs

Can existing Kubeflow pipeline code be reused?

Many pipelines built with compatible Kubeflow Pipelines SDK v2 patterns can be reused. Older syntax and platform-specific components may require updates.

What happens to Kubernetes-specific components?

Components using Kubernetes Secrets, ConfigMaps, persistent volumes, operators, custom resources, or direct cluster APIs must be redesigned using supported Google Cloud services.

Can existing container images be migrated?

Yes. Compatible images can be moved to Artifact Registry, but their entry points, dependencies, permissions, and runtime behavior must be validated.

What happens to pipeline artifacts and metadata?

Artifacts are typically moved to a Cloud Storage pipeline root. Important run metadata and lineage can be mapped to Vertex AI Pipelines, Experiments, and ML Metadata.

How are schedules, permissions, and secrets migrated?

Recurring runs are rebuilt using Vertex AI pipeline schedules or supporting orchestration services. Kubernetes identities and secrets are mapped to Google Cloud service accounts, IAM, and Secret Manager.

How do you validate migrated pipelines?

We compare task order, parameters, artifacts, outputs, conditions, caching, retries, runtime, lineage, failure handling, and downstream results.

Ready to Move from Kubeflow Pipelines to Vertex AI Pipelines?

Assess your pipelines, components, containers, artifacts, schedules, and Kubernetes dependencies to build a practical Vertex AI Pipelines migration roadmap.

Start Your Migration