Metropolis AI: Self-Healing Infrastructure

The project envisions a DevOps solution that mimics the self-regulating (though ultimately flawed) city of Metropolis and incorporates elements of predictive failure and system evolution from 'Hyperion'. Imagine infrastructure as code, but augmented with an AI that learns the 'pulse' of the system. This 'Metropolis AI' observes logs, metrics, and performance indicators across the infrastructure (servers, databases, networks, etc.). It uses techniques inspired by the 'AI Workflow for Companies' scraper project – identifying patterns, anomalies, and correlations from the data streams.

Story & Concept:
Just like in 'Metropolis', the infrastructure relies on interconnected systems, and just like in 'Hyperion', things -will- go wrong. The AI acts as the 'foreman' or 'engineer' of the system, constantly monitoring for signs of impending failure. Instead of relying solely on predefined rules, it learns from historical data, identifying subtle signals that precede outages or performance degradation.

How it Works:
1. Data Ingestion: Collects data from various sources (logs, metrics, alerts) using existing DevOps tools (Prometheus, ELK stack, Grafana, etc.). This data feeds the AI model. A scraper, like the 'AI Workflow for Companies' scraper, can be used to gather best practices and patterns for infrastructure configurations.
2. AI Model: Uses a time-series forecasting model (e.g., LSTM, Prophet) or anomaly detection algorithms (e.g., Isolation Forest) trained on the historical data to predict future system states. A rules engine will manage AI model outcomes, to ensure compliance with policy.
3. Actionable Insights & Automation: When the AI detects a potential problem, it triggers automated remediation steps defined in Infrastructure as Code (IaC) scripts (e.g., Terraform, Ansible). These steps might include scaling resources, restarting services, deploying new configurations, or even rolling back problematic changes.
4. Self-Learning & Adaptation: The AI continuously learns from its actions, improving its prediction accuracy and remediation strategies over time. This creates a closed-loop system where the infrastructure becomes more resilient and self-healing.

Implementation:
- Tech Stack: Python (for AI model), Terraform/Ansible (for IaC), Prometheus/ELK (for data collection), Cloud provider (AWS, Azure, GCP).
- Niche: Focus on a specific type of infrastructure (e.g., Kubernetes clusters, database servers) or industry (e.g., e-commerce, finance) to build specialized models and remediation strategies.
- Low-Cost: Leverage open-source tools and cloud-based services to minimize infrastructure costs. The AI model can be initially trained on synthetic data or publicly available datasets.

Earning Potential:
- SaaS Product: Offer Metropolis AI as a subscription-based service to companies looking to automate their DevOps processes.
- Consulting: Provide consulting services to help companies implement and customize Metropolis AI for their specific needs.
- Training & Education: Create online courses and training materials on how to use Metropolis AI and build self-healing infrastructure.