GitOps-project – Smart Manufacturing Machines Efficiency Prediction

This project implements a GitOps-driven workflow for building, training, and deploying machine learning models that predict the efficiency of manufacturing machines using sensor data. It integrates automation, CI/CD, and containerization principles tailored for Industry 4.0 applications.

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Objectives

• Predict machine efficiency (e.g., OEE – Overall Equipment Effectiveness)
• Analyze real-time sensor data: temperature, vibration, energy use, uptime
• Automate training and deployment via Jenkins and GitOps principles
• Support containerized deployment to cloud or edge via Docker/Kubernetes

Technology Stack

• ML & Data: pandas, scikit-learn, xgboost, numpy
• API: Flask or FastAPI
• Visualization: matplotlib, plotly
• DevOps: Docker, Jenkins, Kubernetes
• GitOps tools (optional): ArgoCD, Flux

-- graph TD GitHub -->|Code push| Jenkins Jenkins -->|Build/Test| Docker Docker -->|Push image| DockerHub DockerHub -->|Trigger| Kubernetes Kubernetes -->|Run|[ML API Service]