AI‑Kubeflow Pipeline‑1
Kubeflow Platform Foundation · Kubernetes‑Native MLOps IaC
Provisioning a production‑style Kubeflow AI platform on Kubernetes using Infrastructure‑as‑Code, namespaces, RBAC, artifact storage, and pipeline runtime setup.
Project summary
AI / MLOps / Platform Engineering
Category
AI · MLOps · Platform Engineering · Kubernetes
Industry: Cross‑industry (Enterprise AI Platforms)
Domain: AI Platform Engineering / Kubernetes‑Native MLOps
Keywords
Runtime
Kubeflow Pipelines (KFP v2), Argo, ML Metadata, MinIO, multi‑env (Dev/Pre‑prod/Prod)
Problem & Objective
Why this platform foundation?
Problem solved
- Manual/ad‑hoc Kubeflow installs → inconsistent environments
- Insecure access controls, misconfigured artifact storage
- Non‑reproducible ML pipeline runtimes across environments
Primary objective
- Reproducible, governed Kubeflow AI platform foundation
- Programmatic bootstrap: cluster prerequisites, KFP runtime, RBAC, MinIO, MLMD
- Consistent, secure execution of ML pipelines across environments
Solution & Architecture
Infrastructure as Code for MLOps
Solution overview
Provisions a Kubernetes cluster and installs Kubeflow Pipelines with MinIO (artifact store), ML Metadata, and Argo Workflows. Platform‑level configs (namespaces, SAs, RBAC, network) are defined as code, creating a standardized foundation for training (Pipeline‑2) and deployment (Pipeline‑3) pipelines.
Cloud/platform: Kubernetes (Minikube dev, portable to EKS/AKS/GKE).
Components: Kubeflow Pipelines v2, Argo Workflows, MinIO, ML Metadata, Ingress, Docker Hub.
Key components & scalability
- KFP v2 runtime
- Argo Workflows engine
- Kubernetes (NS, RBAC)
- MinIO artifact store
- ML Metadata (MLMD)
- Stateless Deployments
- Horizontal pod autoscaling
- Health checks / auto restarts
- Durable artifact storage
- Environment parity via declarative
AI/DevOps & automation
MLOps platform engineering
MLOps focus
- DevOps / MLOps Platform Engineering (Kubeflow AI Platform Foundation)
- Platform bootstrap automation (Kubeflow Pipelines installation)
- Namespace isolation & RBAC
- MinIO wiring, ML Metadata tracking
CI/CD & orchestration
- Kubernetes manifests (platform components)
- Kubeflow Pipelines (ML orchestration)
- Argo Workflows (engine)
- Docker, GitHub Actions (optional CI)
Monitoring & optimisation
- K8s health/readiness probes
- Kubeflow Pipelines UI (run observability)
- Centralized logs (kubectl logs)
- Resource isolation (namespaces/quotas)
- Declarative setup → no drift
Skills & technologies
Proficiency stack
Primary skills
- Kubeflow Platform Engineering (Advanced)
- Kubernetes Platform Ops (Advanced)
- MLOps Platform Design (Advanced)
- Infrastructure‑as‑Code for K8s (Advanced)
Languages & tools
- YAML (K8s/Kubeflow manifests)
- Python (bootstrap scripts)
- Helm/Kustomize (optional)
- Docker, GitHub Actions
Cloud & DevOps tools
Platform foundation & governance
RBAC, namespaces, MLMD
Governance baseline
Kubernetes RBAC (least‑privilege) + isolated namespaces. ML Metadata (MLMD) tracks lineage; MinIO provides durable artifact storage. All defined as code for repeatability.
Challenges
- Correct KFP control plane install
- Wiring MinIO + MLMD for pipelines
- Least‑privilege for workloads
- Reproducible cross‑env setup
Resolutions
- Standardized manifests + version pinning
- Centralized MinIO config for all pipelines
- RBAC policies for pipeline SAs
- Declarative bootstrap + docs
GCP DevOps CI/CD · Architecture mapping
Pipeline‑1 (Platform) constructs
| Architecture Block | GCP CI/CD Construct (Pipeline‑1 – Platform) |
|---|---|
| Source Repository | GitHub (Kubeflow platform bootstrap / IaC repo) |
| Source Trigger | Manual trigger or CI trigger (GitHub Actions) |
| CI Runner | GitHub Actions Linux runner (ubuntu‑latest) |
| Platform Provisioning | Kubernetes manifests / Helm applied to cluster |
| Pipeline Runtime Setup | Kubeflow Pipelines control plane installation + config |
| Artifact Storage | MinIO (datasets, model artifacts, pipeline outputs) |
Pipeline‑1 establishes Kubernetes‑native Kubeflow AI platform foundation, enabling reproducible MLOps infrastructure and governed pipeline execution.
Assets & references
Code, diagrams, study material