Components that I have created for Kubeflow Pipelines. Try them in https://cloud-pipelines.net/pipeline-editor/
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Updated
Oct 1, 2026 - Python
Components that I have created for Kubeflow Pipelines. Try them in https://cloud-pipelines.net/pipeline-editor/
Everything to get industrial kubeflow applications running in production
Scan all Kubeflow pipelines for exposed secrets
An enterprise-grade RAG Chatbot built with AI-assisted development. Features local LLMs (Ollama), LlamaIndex integration, and Entra ID SSO. Designed for secure, air-gapped PDF analysis. 📄
3-stage Kubeflow Pipelines (KFP v2) ML pipeline on Vertex AI: BigQuery extraction → feature engineering → scikit-learn model training with full artifact lineage tracking. Components run in isolated Docker containers.
Standalone Kubeflow Pipelines (KFP 2.16.1) on a local kind cluster — tested on Apple Silicon (M3). Kustomize overlay, sample 3-step pipeline (preprocess → train → evaluate), and an ArgoCD Application for GitOps deployment.
mlopspl 🛠️🔄🚀 : E2E MLOps Vertex AI Pipelines # ML workflow # Kubeflow Pipelines SDK # Cloud Scheduler
My fork of Kubeflow — end-to-end ML workflow orchestration on Kubernetes. Used for building and deploying scalable training and serving pipelines.
KFP v2 pipeline fundamentals practiced locally — components, artifacts, control flow, and end-to-end ML pipeline. Foundation for Google Cloud Vertex AI Pipelines.
End-to-end customer churn prediction on Google Cloud Vertex AI Pipelines (KFP v2). XGBoost + SHAP, deterministic persona segmentation, daily snapshots, drift monitoring.
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