Iris Classification ML Pipeline with Kubeflow

Overview

This repository contains a Kubeflow Pipelines implementation for an Iris flower classification task. It automates data acquisition, preprocessing, model training, and evaluation using Kubeflow on Kubernetes. The pipeline is modular, allowing easy customization for similar ML workflows.

Repository Structure

kubeflow-ml-pipeline/
│── components/
│   ├── data_loader.py         # Fetches the Iris dataset
│   ├── feature_engineering.py # Prepares features and splits dataset
│   ├── model_training.py      # Trains a classification model
│   ├── evaluation.py          # Evaluates model performance
│── pipeline.py                # Defines the Kubeflow pipeline
│── iris_pipeline.yaml         # Compiled pipeline YAML for execution
│── requirements.txt           # Python dependencies
│── README.md                  # Documentation  

Steps to Use This Repository

1. Clone the Repository

git clone https://github.com/your-username/kubeflow-ml-pipeline.git
cd kubeflow-ml-pipeline

2. Set Up Kubeflow

Ensure Kubeflow is installed on a Kubernetes cluster. If using Google Kubernetes Engine (GKE), follow Kubeflow’s official setup guide.

3. Install Dependencies

pip install -r requirements.txt

4. Compile the Pipeline

python pipeline.py

This generates iris_pipeline.yaml, which defines the pipeline structure.

5. Upload to Kubeflow Pipelines UI

1.	Open the Kubeflow UI
2.	Navigate to Pipelines > Upload Pipeline
3.	Select iris_pipeline.yaml and upload

6. Run the Pipeline

1.	Create an Experiment and launch the pipeline
2.	Monitor execution via the Kubeflow UI

For more details, refer to the Kubeflow documentation.