🔐 Network Security 🛡️ | End-to-End MLOps Pipeline 🚀

Welcome to the Network Security Threat Detection project — an end-to-end implementation of a production-grade ML pipeline integrating modern MLOps tools including MLflow, DVC, Docker, GitHub Actions, CI/CD, and Dagshub.

💡 Designed for real-world use cases like intrusion detection using machine learning — deployable, trackable, reproducible, and scalable!

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📊 Problem Statement

In this project, we tackle the problem of network intrusion detection using ML algorithms. Our goal is to build a pipeline that:

• Ingests network traffic data
• Preprocesses and extracts meaningful features
• Trains a model to classify normal vs suspicious activity
• Deploys the model with full CI/CD and MLOps support

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🧪 ML Pipeline Components

🛠️ Data Versioning with DVC

• ✅ Tracks raw and processed datasets
• ✅ Pipeline defined in dvc.yaml
• ✅ Remote storage: DagsHub

🧪 Experiment Tracking with MLflow

• 🎯 Log metrics, parameters, and artifacts
• 📊 Compare multiple models & runs visually
• 🧠 Integrate with sklearn pipeline easily

🐳 Containerization with Docker

• Reproducible environments
• Easy deployment across dev/stage/prod

🔄 CI/CD with GitHub Actions

• Every push or PR triggers:
  • ✅ Code linting
  • ✅ Unit tests
  • ✅ DVC pipeline execution
  • ✅ MLflow tracking
  • ✅ Docker build & push

📡 Deployment Ready

• Containerized inference API (FastAPI or Flask)
• Ready for deployment on AWS/GCP/Azure or Heroku

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🚀 Tools & Technologies Used

Category	Tools
👨‍💻 Programming	Python, Pandas, NumPy, Scikit-learn
📦 ML Lifecycle	MLflow, DVC, Dagshub, Hydra
🧱 MLOps/CI-CD	GitHub Actions, Docker, YAML, Pytest
🌐 Visualization	Matplotlib, Seaborn, MLflow UI
🔁 Version Control	Git, GitHub

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⚙️ Run the Project Locally

# 1. Clone the repo
git clone https://github.com/yourusername/network-security-mlops.git
cd network-security-mlops

# 2. Set up DVC
dvc pull   # Pull data from remote storage
dvc repro  # Reproduce the entire pipeline

# 3. Track experiments
mlflow ui  # Launch MLflow dashboard at http://127.0.0.1:5000

# 4. Build Docker container
docker build -t network-mlops-app .
docker run -p 8000:8000 network-mlops-app

📊 Dataset Used

This is the dataset used to build and train the machine learning model in this project.
Download it from the link below:

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📈 MLflow Example Output

• 🔢 Accuracy, Precision, Recall, ROC Curve
• 📁 Model artifact: RandomForestClassifier.pkl
• 📊 Visual run comparison dashboard

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Monitoring and Experiment Tracking

For seamless monitoring of the project’s DVC pipelines and MLflow experiments, kindly visit the Dagshub repository linked below:

🔗 View Dagshub Repository

📷 MLflow Tracking UI

Here's a preview of how the experiments and metrics are logged in MLflow:

🔁 Model Metrics

Below is the Models metrics That have used in this model traning

📊 Comparision Charts

Below is a visualization of metrics like accuracy, precision, recall over different runs:

🔁 DVC Pipeline from Dagshub

DVC pipeline stages and tracking interface hosted on Dagshub:

🚧 CI/CD Pipeline (GitHub Actions)

.github/workflows/mlops.yml
Runs the full pipeline on every push:

• ✅ Lint + Tests
• ✅ DVC repro
• ✅ MLflow log
• ✅ Docker image push

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🧠 Future Improvements

• ✨ Add LSTM or Deep Learning models
• 📦 Convert to FastAPI microservice
• 🔐 Add role-based access control for logs
• 📊 Grafana + Prometheus monitoring
• ☁️ Deploy to AWS Sagemaker or GCP AI Platform
• ✨ CICD Pipeline

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⭐️ Show your support

If you liked this project, please consider giving it a ⭐️ and sharing with others!

🛡️ Empowering network security with reproducible ML pipelines!

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🖥️ Model Deployment Options

Choose either of the following options to serve your trained model:

🎨 Streamlit Dashboard

• An interactive web interface to test your model
• Upload CSV or manually enter input features
• See predictions live, with metrics and visualizations

streamlit run src/app_streamlit.py

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🚀 Flask API

• A production-grade REST API endpoint to serve predictions
• Send POST requests with JSON payload
• Easily integrate with any web or mobile frontend

python src/app_flask.py

💡 Whether you're demoing the model in a web dashboard (Streamlit) or deploying via API (Flask), this project gives you flexibility to move fast in any real-world environment.

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🧪 DEMO & Practice

This project structure and workflow was inspired by Krish Naik sir’s teachings on practical MLOps and end-to-end ML deployment.

💡 I’ve extended and personalized it with full MLOps integration, CI/CD, and deployment options for real-world use cases in Network Security.

🧑‍🏫 Demo Guidance:

You can also explore Krish Naik’s YouTube tutorials on:

• DVC + MLflow
• MLOps project pipelines
• Dockerized ML apps
• Streamlit deployment
• GitHub Actions for CI/CD

▶️ Practice this project hands-on by cloning and running each stage step-by-step.
Contributions are welcome to enhance this open-source solution further!

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💬 “Learning by doing is the fastest path to mastery.” — Thanks to Krish Naik sir for being a constant inspiration to ML learners.

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