https://github.com/kiran8053/smart-machines-efficiency-prediction

An end-to-end MLOps project that predicts the efficiency of industrial smart machines—High, Medium, or Low—using real-time operational data. Built with Flask, Docker, and Kubernetes, and automated via a Jenkins CI/CD pipeline, it showcases the complete lifecycle from data preprocessing and model training to scalable deployment.
https://github.com/kiran8053/smart-machines-efficiency-prediction

ci-cd classification-model data-science deployment docker flask industrial-iot jenkins kubernetes machine-learning mlops model-serving predictive-maintenance python

Last synced: about 1 month ago
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An end-to-end MLOps project that predicts the efficiency of industrial smart machines—High, Medium, or Low—using real-time operational data. Built with Flask, Docker, and Kubernetes, and automated via a Jenkins CI/CD pipeline, it showcases the complete lifecycle from data preprocessing and model training to scalable deployment.

• Host: GitHub
• URL: https://github.com/kiran8053/smart-machines-efficiency-prediction
• Owner: Kiran8053
• Created: 2025-08-13T06:36:59.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2025-08-13T07:24:28.000Z (about 2 months ago)
• Last Synced: 2025-08-13T09:07:25.919Z (about 2 months ago)
• Topics: ci-cd, classification-model, data-science, deployment, docker, flask, industrial-iot, jenkins, kubernetes, machine-learning, mlops, model-serving, predictive-maintenance, python
• Language: Jupyter Notebook
• Homepage:
• Size: 18.5 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
*Smart Machines Efficiency Prediction*

## Overview

This project predicts the efficiency category of industrial smart machines — High, Medium, or Low — based on operational, environmental, and performance metrics.

It is implemented as a Flask web application, containerized with Docker, deployable to Kubernetes, and CI/CD-enabled via Jenkins.

## Features

Machine Learning model trained to classify machine efficiency.

Web-based UI for entering operational parameters and getting instant predictions.

Containerized using Docker for portability.

Kubernetes deployment manifests for scaling in production.

Jenkins pipeline for automated build and deployment.

## Project Structure

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.

- application.py         # Flask application with prediction logic

-  notebook.ipynb         # Model training and experimentation

- requirements.txt       # Python dependencies

- setup.py               # Package configuration

- Dockerfile             # Docker image build instructions

- Jenkinsfile            # Jenkins CI/CD pipeline script

- deployment.yaml        # Kubernetes Deployment manifest

- service.yaml           # Kubernetes Service manifest

├── artifacts/

   -- models/model.pkl   # Trained ML model

   -- processed/scaler.pkl # Scaler for preprocessing

   -- templates/

    --- index.html         # HTML template for Flask UI

    

## Model Details

# Input Features

Operation Mode

Temperature (°C)

Vibration (Hz)

Power Consumption (kW)

Network Latency (ms)

Packet Loss (%)

Quality Control Defect Rate (%)

Production Speed (units/hr)

Predictive Maintenance Score

Error Rate (%)

Year, Month, Day, Hour

# Output Labels

High efficiency

Medium efficiency

Low efficiency

## Installation & Setup

1️⃣ Clone the repository

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git clone https://github.com/Kiran8053?tab=repositories.git

cd "Kiran8053/Smart Machines Efficiency Prediction"

2️⃣ Create & activate a virtual environment

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python -m venv venv

source venv/bin/activate   # macOS/Linux

venv\Scripts\activate      # Windows

3️⃣ Install dependencies

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pip install -r requirements.txt

4️⃣ Run the application

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python application.py

Access the app at http://localhost:5000

## Docker Deployment

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# Build the Docker image

docker build -t smart-machines-efficiency .

# Run the container

docker run -p 5000:5000 smart-machines-efficiency

☸ Kubernetes Deployment

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kubectl apply -f deployment.yaml

kubectl apply -f service.yaml

Once deployed, the service will expose the prediction API/UI.

## CI/CD Pipeline

Jenkinsfile defines automated stages:

Code checkout

Build & test

Docker image build & push

Kubernetes deployment

Can be integrated with GitHub webhooks for continuous delivery.

## Example Prediction

Sample Input:

makefile

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Operation_Mode: 1

Temperature_C: 75

Vibration_Hz: 60

Power_Consumption_kW: 120

Network_Latency_ms: 10

Packet_Loss_%: 0.2

Quality_Control_Defect_Rate_%: 1.5

Production_Speed_units_per_hr: 500

Predictive_Maintenance_Score: 0.85

Error_Rate_%: 0.3

Year: 2025

Month: 8

Day: 13

Hour: 14

Sample Output:

makefile

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Efficiency: High

📜 License

This project is licensed under the MIT License.