Vertex AI Training & Evaluation Pipeline
Kubeflow Pipelines v2 · Production‑grade MLOps on GCP
Production‑grade ML training, evaluation, gating, and conditional deployment pipeline on Google Vertex AI using Kubeflow Pipelines (KFP v2). Enforces model quality, tracks lineage, and deploys only validated models.
Project Summary
AI + MLOps + Cloud Platform Engineering
Category
AI/ML · MLOps · Platform Engineering
Industry
Cross‑industry Enterprise AI Platform
MLOps Focus
Training · Evaluation · Gating · Conditional Deploy
Key Technologies & Concepts
ML/AI platform primitives
Problem & Objective
Why this pipeline exists
Problem
Manual, notebook‑driven ML workflows lack reproducibility, governance, automated evaluation gates, and production discipline. No structured way to enforce model quality before deployment in GCP.
Objective
Build a production‑grade, automated ML training/evaluation pipeline on GCP that enforces quality gates, tracks lineage, and conditionally deploys models to Vertex AI endpoints using native MLOps primitives.
Solution & Architecture
Vertex AI native orchestration
Overview
Vertex AI Pipelines (KFP v2) orchestrates data preparation, model training (RandomForest), evaluation (ROC, confusion matrix, accuracy), quality gating, conditional deployment to Vertex AI Endpoints, and scheduled retraining.
Skills & Technologies
ML/platform engineering stack
Primary (Advanced)
- MLOps Architecture
- Vertex AI Pipelines / KFP v2
- Cloud AI Platform Engineering
- Production ML Workflow Design
Secondary
- Kubeflow Pipelines SDK
- scikit‑learn · Vertex AI SDK
- GCS · IAM · Workload Identity
- GitHub Actions (CI trigger)
Languages & DevOps
Pipeline Execution & Governance
Conditional gates, lineage, scheduling
Execution
- Manual / CI trigger → Vertex AI Pipeline run
- KFP v2 components: data prep, training, evaluation, deploy
- Artifacts stored in GCS, metrics in Vertex AI Metadata
Governance
- Explicit evaluation gate (accuracy/ROC threshold)
- Conditional pipeline branch: deploy only if gate passes
- Model versioning in Vertex AI Model Registry
- IAM least‑privilege + Workload Identity Federation
Challenges & Resolutions
Wiring KFP v2 components → Vertex Pipelines: used native KFP interfaces.
ROC/metrics logging: sanitized inputs for Vertex metrics APIs.
Conditional gates: pipeline condition with threshold check.
Model format for serving: packaged as Vertex‑compatible artifact.
Notebook to production: refactored into pipeline components.
GCP CI/CD · Architecture & Mapping
MLOps constructs to KFP/Vertex
| Architecture Block | GCP / KFP v2 Construct |
|---|---|
| Source Repository | GitHub (vertex-ai-mlops-kfp2) |
| Source Trigger | Manual / GitHub Actions (CI) |
| CI Runner | ubuntu‑latest (optional) |
| Pipeline Orchestration | Vertex AI Pipelines (KFP v2) |
| Data Processing | Python component (Pandas + sklearn) |
| Training | Vertex AI Training (custom job) |
| Evaluation | Python component (ROC, CM, accuracy) |
| Quality Gate | Conditional + Vertex AI Metadata check |
| Model Upload | Vertex AI Model Registry |
| Deployment | Vertex AI Endpoint (conditional) |
| Artifact Store | GCS · Metadata Store |
Assets & References
Code, diagrams, study material
Repository
vertex-ai-mlops-kfp2: full pipeline code, components, deployment specs.
github.com/Rajesh-Arigala/…Study Material Resources
Official docs, restricted KFP guides, Colab notebooks
Request Study Material