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Deployment of Predictive Maintenance Aircraft Engine System
- The Predictive Maintenance Aircraft Engine system is designed to leverage real-time sensor data from aircraft engines to predict when maintenance is needed.
- This document provides a detailed overview of the deployment process for the system, covering the full-stack architecture, Docker setup, and steps to deploy the application using Docker and Docker Compose.
- The system is composed of two key components: Frontend (Dash) and Backend (Flask).
- The backend is a RESTful API implemented using Flask and the frontend is built using Dash.
- To streamline deployment and ensure that the application runs consistently across different environments, both the frontend and backend are containerized using Docker.
- To deploy the application, clone GitHub repository, build and start both the backend and frontend services simultaneously, and access the services via endpoint URLs.
- For production deployments, consider using orchestration tools like Kubernetes to handle scaling, resource management, and security.
- Model management, monitoring and logging, security, and continuous integration and deployment (CI/CD) are some additional considerations for deploying the system in a production environment.
- By combining Flask for the backend API, Dash for interactive visualizations, and Docker for containerization, this solution offers a reliable and scalable solution for optimizing aircraft engine maintenance operations.
- With further enhancements, the Predictive Maintenance Aircraft Engine system can serve as a critical tool for improving aircraft engine maintenance operations and reducing unplanned downtime.
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