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Why Choose This Project?

With the growing need for automation in machine learning workflows, Kubeflow on Google Cloud offers an ideal platform for building end-to-end, production-grade ML pipelines. This project is ideal for those looking to gain experience with MLOps, CI/CD for ML, cloud orchestration, and scalable deployment of ML models.

What You Get

A full ML workflow: data ingestion → training → evaluation → deployment
Automated pipeline orchestration using Kubeflow Pipelines
Reproducible and version-controlled model builds
Scalable compute via GKE (Google Kubernetes Engine)
Integration with GCP services like BigQuery, Cloud Storage, Vertex AI, etc.
Visualization and monitoring of training runs

Key Features

Feature	Description
End-to-End ML Pipeline	Covers all stages from data to deployment
Data Preprocessing Component	Cleans and transforms raw data
Model Training & Evaluation	Custom ML model training and metrics evaluation
Pipeline Versioning	Track versions, parameters, and artifacts
Model Registry Integration	Push trained models to Vertex AI Model Registry
Model Deployment	Serve models using Vertex AI or custom Kubernetes
CI/CD for ML	Git → Build → Train → Deploy automation
Visual Pipeline UI	Monitor pipeline execution and logs in dashboard
Hyperparameter Tuning	Optional tuning using Katib or Vertex AI Vizier
Custom Components	Modular pipeline steps built with Python SDK

Technology Stack

Layer	Tools/Services
Data Ingestion	BigQuery / GCS / Cloud Pub/Sub
Data Processing	Python, Pandas, Apache Beam (optional)
Model Training	TensorFlow / Scikit-Learn / XGBoost
Pipeline Orchestration	Kubeflow Pipelines on GKE
Compute Cluster	Google Kubernetes Engine (GKE)
Model Deployment	Vertex AI Endpoint / KServe on GKE
Storage	Google Cloud Storage (datasets, models)
Metadata Tracking	ML Metadata + Pipeline Artifacts
Monitoring	Stackdriver + TensorBoard
CI/CD Integration	Cloud Build + GitHub + Tekton (optional)

Google Cloud Services Used

Service	Purpose
Google Kubernetes Engine (GKE)	Host Kubeflow and pipelines
Cloud Storage (GCS)	Store datasets, model artifacts
BigQuery	Structured data warehouse for analytics
Cloud Build	CI/CD for pipeline builds or Docker containers
Vertex AI	Optional model deployment + model registry
Cloud Logging / Monitoring	Track pipeline health, debug errors
IAM & VPC	Secure access and isolation of services
Artifact Registry	Store container images of pipeline components

Working Flow

Data Ingestion
- Raw data is uploaded to Google Cloud Storage or ingested from BigQuery
- Optional: data streams from Pub/Sub are processed into a DataLake
Pipeline Execution (Kubeflow)
- Triggered manually or via Git push/CI tool
- Data preprocessing → training → evaluation → output artifacts
- Each step is containerized and tracked
Model Registry & Deployment
- Validated models pushed to Vertex AI Model Registry
- Endpoint deployed on Vertex AI or GKE-based model server
Monitoring & Iteration
- Logs, metrics, and errors tracked in Stackdriver
- Developers use the Kubeflow UI to analyze runs and retry failed steps
- Next versions are triggered with new data or parameters

Main Modules

Module	Description
Data Loader Component	Loads and optionally validates data from GCS/BigQuery
Preprocessing Module	Cleans and transforms data (missing values, encoding, etc.)
Training Module	Trains ML model using frameworks like TF, XGB, or Scikit
Evaluation Component	Computes accuracy, precision, recall, etc.
Model Validation Step	Ensures model meets performance thresholds
Model Registry Pusher	Uploads model artifact to Vertex AI Model Registry
Deployer Module	Deploys trained model to a serving endpoint
Notification Component	Optional email/slack alert for pipeline success/failure
Parameter Store	Allows tuning via variables passed during pipeline run

Security Features

Private GKE cluster to run Kubeflow securely
IAM policies to restrict access to model data and pipelines
Service Accounts with minimum required privileges
Data Encryption at rest and in transit (GCS, BigQuery)
Audit Logs via Cloud Logging for all pipeline events
Vertex AI with built-in model monitoring and access control

Visualization Options

Tool	Purpose
Kubeflow UI	Graphical view of pipeline DAG and logs
TensorBoard	Monitor training metrics (loss, accuracy, etc.)
Cloud Monitoring	Track CPU, memory, I/O usage of pipeline steps
ML Metadata Viewer	View lineage of datasets, models, and outputs
Vertex AI Dashboard	Manage models, endpoints, experiments