This document outlines the infrastructure setup, including server and device

requirements, technology stack, and other considerations you might need to address
using open-source tools and technologies.
Infrastructure Setup:
Servers and Devices Requirements:
1. Development Environment: Each participant or team member should have
access to a development environment. This could be a personal computer or
laptop with sufficient processing power and memory.
2. Cloud Servers: Depending on your solution's complexity, you might need
cloud servers to train and deploy machine learning models, host databases, and
run the application. Consider using cloud providers like AWS, Google Cloud, or
Azure. Make sure you have instances with suitable specifications for training
and hosting.
3. Database Server: You'll need a database server to store the incident-
resolution database and historical data. You can use open-source databases
like PostgreSQL, MySQL, or even NoSQL databases like MongoDB if the data
structure requires flexibility.
4. Web/Application Server: For hosting the AI-powered resolution platform
and the user interface, you'll need a web/application server. Popular choices
include Apache, Nginx, or you can consider containerization with tools like
Docker and orchestration with Kubernetes.
Technology Stack:
1. Programming Languages: Depending on your team's expertise and
preferences, you might use languages like Python for AI/ML, backend
development, and scripting, while using JavaScript for frontend development.
2. Machine Learning Frameworks: Utilize open-source ML frameworks like
TensorFlow, PyTorch, or scikit-learn for building and training AI models for
incident resolution.
3. Backend Frameworks: Choose a backend framework to build your AI-
powered platform. Flask and Django (both Python frameworks) are popular
choices due to their simplicity and flexibility.
4. Frontend Frameworks: For the user interface, you can use frameworks like
React, Angular, or Vue.js for building dynamic and interactive UIs.
5. Database Management: For database management, PostgreSQL or MySQL
can be used for structured data, while MongoDB could be suitable for
unstructured or semi-structured data.
6. Version Control: Use Git for version control. Host your repository on platforms
like GitHub or GitLab.
 7. CI/CD: Set up a continuous integration and continuous deployment (CI/CD)
pipeline using tools like Jenkins, Travis CI, or GitLab CI/CD to automate testing
and deployment.
8. Containerization: Docker can help you create reproducible environments for
your application, making it easier to manage dependencies.
9. Monitoring and Logging: Use tools like Prometheus for monitoring and
Grafana for visualization. For logging, you can consider the ELK stack
(Elasticsearch, Logstash, Kibana).
10. APIs: Implement RESTful APIs for communication between frontend and
backend components.
Other Considerations:
1. Data Collection and Storage: Set up a data collection mechanism to gather
incident data and resolution history. Ensure proper data preprocessing before
storing in the database.
2. Model Training and Deployment: Train your AI models on historical data to
predict resolutions. Deploy the trained models on a server or cloud instance.
3. Self-Healing Mechanism: Implement the logic for self-healing based on
frequency and historical success rates. This might involve creating a decision-
making mechanism that automatically triggers resolution actions.
4. User Interface: Design a functional user interface where users can report
incidents, view recommended resolutions, and interact with the self-healing
system.
5. Security: Implement security measures to protect user data and prevent
unauthorized access. Use HTTPS, encryption, and proper authentication.
6. Documentation: Keep your codebase and setup well-documented. Explain
the architecture, data flow, and how different components interact.
7. Scalability: Design your solution to be scalable so that it can handle increased
workloads as the number of incidents grows.
Open-Source Resources:
1. Machine Learning Frameworks:
• TensorFlow: A popular open-source machine learning framework
developed by Google.
• PyTorch: An open-source machine learning framework with a strong
focus on dynamic computation graphs.
2. Backend Frameworks:
 • Flask: A lightweight Python web framework for building RESTful APIs
and web applications.
• Django: A high-level Python web framework that encourages rapid
development and clean, pragmatic design.
3. Frontend Frameworks:
• React: A JavaScript library for building user interfaces, developed by
Facebook.
• Angular: A TypeScript-based open-source framework for building
dynamic web applications.
• Vue.js: A progressive JavaScript framework for building user interfaces.
4. Database Management:
• PostgreSQL: An open-source relational database management system.
• MySQL: An open-source relational database system known for its speed
and reliability.
• MongoDB: An open-source NoSQL database for managing unstructured
or semi-structured data.
5. Version Control:
• Git: An open-source distributed version control system.
• GitHub: A web-based platform for hosting Git repositories.
6. Containerization:
• Docker: An open-source platform to develop, ship, and run applications
in containers.
7. Monitoring and Logging:
• Prometheus: An open-source monitoring and alerting toolkit.
• Grafana: An open-source platform for monitoring and observability with
beautiful visualizations.
8. CI/CD:
• Jenkins: An open-source automation server for building, testing, and
deploying code.
• Travis CI: A continuous integration platform that integrates well with
GitHub.
• GitLab CI/CD: A continuous integration and continuous deployment
platform provided by GitLab.
Steps to Set Up:
1. Setting Up a Development Environment:
• Install Python, Node.js (for frontend development), and other necessary
tools on your local machine.
2. Cloud Servers:
• Sign up for a cloud provider (e.g., AWS, Google Cloud, Azure).
• Create instances with suitable specifications for development and
testing.
3. Database Server:
• Install and configure your chosen database system on a cloud instance
or a server.
• Create the necessary tables and schemas to store incident data.
4. Backend Setup:
• Choose a backend framework (e.g., Flask, Django).
• Set up a virtual environment for your Python project.
• Install required packages using pip.
• Create API endpoints for data collection, resolution prediction, and self-
healing actions.
5. Frontend Setup:
• Choose a frontend framework (e.g., React, Angular, Vue.js).
• Initialize your frontend project using the respective CLI.
• Design and develop UI components for incident reporting and resolution
display.
6. Model Training and Deployment:
• Use your chosen ML framework (e.g., TensorFlow, PyTorch) to develop
and train your AI models.
• Save trained models and deploy them using the backend server.
7. Self-Healing Mechanism:
• Implement logic to calculate resolution recommendations based on
frequency and historical data.
• Design a decision-making process that triggers self-healing actions.
8. User Interface:
 • Integrate the frontend with the backend using API calls.
• Create forms for users to report incidents and display recommended
resolutions.
9. Security and Authentication:
• Implement user authentication and authorization mechanisms to protect
sensitive data.
10. CI/CD Pipeline:
• Set up a CI/CD pipeline using tools like Jenkins, Travis CI, or GitLab
CI/CD.
• Automate testing, building, and deployment processes.
11. Monitoring and Logging:
• Install Prometheus for monitoring and Grafana for visualizations.
• Configure logging for your application using appropriate libraries.
12. Documentation and Version Control:
• Create a Git repository for your project on platforms like GitHub or
GitLab.
• Keep your codebase organized and well-documented using README
files, code comments, and documentation tools.
Remember, the key to winning this hackathon is not just the technology you use, but
how well your solution addresses the problem, the innovation you bring in, and the
clarity of your presentation and documentation. Good luck with your hackathon
project!