This project involved the design and development of a Python-based automation platform tailored for linear infrastructure engineering workflows. The platform integrates multiple components to provide a robust solution for data ingestion, modeling, analysis, and stakeholder interaction.

The system features:

  • Live data ingestion every 5 minutes from operational sources
  • Automated reliability assessments
  • “What-if” scenario modeling to evaluate potential system responses
  • Predictive maintenance models powered by machine learning
  • Interactive dashboards for real-time results and reporting

It was designed to run in scalable Docker containers, making deployment and parallel execution highly efficient.

Key Contributions

  • Built a modular automation pipeline using Python, Docker, and modern data tools
  • Integrated real-time data ingestion with analytical processing
  • Developed ML models to forecast component reliability and failure risk
  • Enabled scenario-based simulations to support planning decisions
  • Delivered an interactive dashboard interface for users

Impact

  • ⚡ Reduced manual engineering effort through automation
  • 📈 Improved forecasting accuracy for infrastructure reliability
  • 📊 Empowered stakeholders with actionable insights via dashboards and auto-generated reports

Tools & Technologies

  • Python, Pandas, Scikit-learn
  • Docker & Docker Compose
  • REST APIs for data feeds
  • Plotly Dash / Streamlit (dashboarding)
  • Git, CI/CD pipelines