# Kubeflow Pipelines v2 & Data Science Pipelines on RHOAI: Declarative Orchestration and MLOps Reproducibility

> Discover enterprise MLOps orchestration with Kubeflow Pipelines v2 (KFP) and Data Science Pipelines on Red Hat OpenShift AI: containerized workflows, S3 artifact lineage tracking, and automated model lifecycles.

- **Date de publication** : 2026-09-29T08:30:00.000Z

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In enterprise artificial intelligence initiatives, transitioning from exploratory Jupyter notebooks to reliable, automated production workflows represents a major engineering challenge. Without a standardized orchestration framework, platform teams quickly encounter critical bottlenecks in training reproducibility, dataset lineage tracking, heterogeneous compute resource management (CPU/GPU), and tangled step dependencies.

With the native integration of **Kubeflow Pipelines v2 (KFP)** within **Data Science Pipelines** on **Red Hat OpenShift AI (RHOAI)**, platform engineers gain an enterprise-grade, declarative MLOps orchestration framework designed to streamline and automate the complete model lifecycle.

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### 1. Declarative Architecture & Data Science Pipelines Operator
On OpenShift AI, workflow orchestration is powered by the **Data Science Pipelines Operator (DSPO)**, which deploys and manages dedicated pipeline infrastructure per project namespace:
* **Python KFP v2 Specification:** Pipelines are authored in Python using the `kfp` v2 SDK and compiled into standardized, auditable YAML Intermediate Representation (IR) manifests.
* **Modular Containerized Execution:** Each pipeline step (*component*) runs in an isolated OpenShift Pod container, guaranteeing clean encapsulation of software dependencies (CUDA versions, PyTorch, scikit-learn, data transformation libraries).
* **Namespace Multi-Tenancy & Isolation:** Every Data Science project receives its own dedicated pipeline server, segregating secrets, storage credentials, and compute quotas without risk of multi-team interference.

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### 2. S3 Artifact Management & Lineage Tracking
A resilient machine learning pipeline requires immutable tracking of data flows and generated assets:
* **Object Storage Artifact Persistence (S3 / ODF):** Data Science Pipelines natively interface with S3-compatible object stores (provided by **OpenShift Data Foundation / Ceph RGW** or MinIO) to store versioned datasets, model checkpoints, and evaluation metrics.
* **Lineage & Metadata Tracking:** Every execution (*run*) automatically records input and output metadata (hyperparameters, training curves, validation metrics). This comprehensive lineage supports strict compliance and auditability requirements (such as the EU AI Act).
* **Intelligent Step Caching:** KFP v2 evaluates cryptographic hashes of inputs and component logic. When an upstream step has already been executed with identical inputs, its cached output is reused instantly (`enable_caching=True`), drastically cutting execution latency and expensive GPU computing costs.

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### 3. Granular Resource Allocation & GPU Scheduling
AI pipeline execution demands precise workload sizing across heterogeneous compute infrastructure:
* **Declarative GPU & CPU Limits:** Individual components dynamically request hardware accelerators through Kubernetes resource APIs (`kubernetes.add_resource_limit(task, 'nvidia.com/gpu', '1')`) or target dedicated worker nodes using node selectors and tolerations.
* **Advanced Flow Control:** Built-in support for parallel loops (`dsl.ParallelFor`), conditional branching (`dsl.Condition`), and exit handlers ensures clean temporary resource deallocation and automated alerting upon execution failure.

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### 4. End-to-End MLOps: From Pipeline to KServe / vLLM Serving
The true power of an automated pipeline lies in seamlessly closing the loop from training to inference:
* **Model Registry Ingestion:** Upon successful validation against performance thresholds, the pipeline registers the candidate model into the central RHOAI Model Registry.
* **Automated InferenceService Rollout:** The concluding pipeline step declaratively deploys or updates **KServe** InferenceServices or **vLLM** ServingRuntimes, ensuring zero-downtime continuous deployment into production.
* **GitOps & CI/CD Integration:** Pipeline definitions versioned in Git repositories can be triggered via webhooks from Tekton / OpenShift Pipelines whenever new data drops or code updates occur.

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### 5. Curriculum Connections & Aperta Scientia Programs
* **AS300 Program (AI Platform Engineer — 399h):** Design end-to-end MLOps pipelines with Kubeflow Pipelines v2 and Data Science Pipelines on RHOAI, orchestrate distributed GPU training workloads, manage artifact lineage, and automate KServe/vLLM serving rollouts (module **AI267** Red Hat OpenShift AI and MLOps).
* **AS200 Program (DevOps Platform Engineer — 399h):** Administer the underlying cloud-native infrastructure, configure ODF storage classes (Ceph S3/RBD), enforce RBAC project policies, and integrate automated delivery pipelines via OpenShift Pipelines and GitOps (modules **DO280** and **DO380**).

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### 6. Technical References & Documentation
* **Red Hat OpenShift AI Documentation:** [Red Hat Product Documentation - Working with Data Science Pipelines](https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/)
* **Kubeflow Pipelines Project:** [Kubeflow Pipelines v2 SDK & Architecture Guide](https://www.kubeflow.org/docs/components/pipelines/v2/)
* **CNCF Cloud Native AI Working Group:** [CNCF AI/ML Whitepaper & Reference Architectures](https://github.com/cncf/tag-runtime)

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*Source : https://aperta.io/radar/2026-09-29-kubeflow-pipelines-v2-rhoai-reproducible-mlops/ — Aperta Scientia Tech Radar*