_______  _______  _______ _________ _______  _______ _________
(  ____ )(  ____ )(  ___  )\__    _/(  ____ \(  ____ \\__   __/
| (    )|| (    )|| (   ) |   )  (  | (    \/| (    \/   ) (   
| (____)|| (____)|| |   | |   |  |  | (__    | |         | |   
|  _____)|     __)| |   | |   |  |  |  __)   | |         | |   
| (      | (\ (   | |   | |   |  |  | (      | |         | |   
| )      | ) \ \__| (___) ||\_)  )  | (____/\| (____/\   | |   
|/       |/   \__/(_______)(____/   (_______/(_______/   )_(   
                                                               
 _______           _        _______  _______  _______  _______ 
(  ____ \|\     /|( (    /|(  ___  )(  ____ )(  ____ \(  ____ \
| (    \/( \   / )|  \  ( || (   ) || (    )|| (    \/| (    \/
| (_____  \ (_) / |   \ | || (___) || (____)|| (_____ | (__    
(_____  )  \   /  | (\ \) ||  ___  ||  _____)(_____  )|  __)   
      ) |   ) (   | | \   || (   ) || (            ) || (      
/\____) |   | |   | )  \  || )   ( || )      /\____) || (____/\
\_______)   \_/   |/    )_)|/     \||/       \_______)(_______/

🔍 Project Overview

Although a very diverse project, this project is, at its heart, about building a small form factor (SFF) home IT lab with enterprise-capabilities in an economical and power-efficient infrastructure for developing your IT skills. What you do, build, and learn with it is entirely up to you.

Regardless, welcome to Project Synapse, my name is Don, and I do cloud AI stuff.

AI-Ops Home Lab is an ongoing experiment in integrating AI technologies with DevOps practices in a home lab environment. This project aims to explore the synergies between artificial intelligence, infrastructure automation, and continuous integration/deployment pipelines.

There is a considerable amount of information in the wiki such as current lab stats, infrastructure charts, and more.

📚 Table of Contents

• Project Overview
• Current Status: Active Development
• Key Features
• Technology Stack
• Recommendations
• Getting Started
• Ethical Considerations
• Limitations and Disclaimers
• About Me
• Contributing
• License
• Acknowledgments
• Contact
• Sponsors
• 📖 Wiki

🛠️ Current Status: Active Development

This project is in active development and should be considered experimental. While we strive for stability and reliability, some components may be in various stages of implementation or testing.

✨ Key Features

• AI Integration: Leveraging AI models for infrastructure management and decision support.
• DevOps Practices: Implementing CI/CD pipelines, Infrastructure as Code (IaC), and automated testing.
• Home Lab Environment: Utilizing consumer-grade and/or used enterprise hardware to emulate enterprise-level setups.
• Ethical AI Development: Focusing on responsible AI practices and transparency.

🚀 Technology Stack

• Virtualization: Proxmox VE
• Containerization: Docker, Kubernetes
• AI/ML: TensorFlow, PyTorch, DeepInfra
• Infrastructure Management: Ansible, Terraform
• Version Control: Git with GitHub and Gitea
• Monitoring: Prometheus, Grafana, Teams Rooms, Gotify, AlertManager
• CI/CD: Azure DevOps, Gitea, GitHub
• Databases: MariaDB, PostgreSQL, InfluxDB, Neo4j, Redis
• Message Queuing: RabbitMQ, Apache Kafka
• API Gateway: Kong
• Load Balancing: HAProxy, NGINX
• Security: HashiCorp Vault (on-premises), Azure Key Vault
• Logging: ELK Stack (Elasticsearch, Logstash, Kibana)
• Configuration Management: Puppet
• Asset Management: DataGerry
• Automation: Ansible, Azure Pipelines, Logic Apps

📝 Recommendations

1. Start with core services like Proxmox VE for virtualization and Docker for containerization.
2. Implement a robust monitoring solution using Prometheus, Grafana, and AlertManager early in the project.
3. Set up version control with Git, using GitHub for public repositories and Gitea for internal use.
4. Choose databases based on your specific use cases, but consider starting with PostgreSQL for relational data and Redis for caching.
5. Implement CI/CD pipelines using Azure DevOps and Azure Pipelines, integrating with GitHub and Gitea for a comprehensive development workflow.
6. Use Ansible for configuration management and automation tasks.
7. Implement HashiCorp Vault for on-premises secrets management and Azure Key Vault for cloud-based secrets.
8. As you progress, introduce more specialized tools like Neo4j for graph data and Apache Kafka for event streaming.
9. Utilize DeepInfra for advanced AI/ML capabilities in your infrastructure management.
10. Set up DataGerry for comprehensive asset management across your lab environment.

🧑‍💻 Getting Started

(Note: This section will be expanded as the project progresses)

1. Clone the repository.
2. Review the documentation in the docs folder.
3. Check the CONTRIBUTING.md file for guidelines on how to contribute.

🌐 Ethical Considerations

This project is committed to the responsible development and use of AI technologies. We adhere to the following principles:

• Transparency in AI decision-making processes.
• Privacy protection and data minimization.
• Regular ethical audits of AI components.
• Open discussion of potential biases and limitations.

⚠️ Limitations and Disclaimers

• This project is not intended for production use without thorough testing and validation.
• The AI components are experimental and may not always produce optimal results.
• Performance may vary depending on hardware configurations.

👨‍💼 About Me

I'm Don, and I've been involved in computer hardware and IT for over four decades now. My journey has taken me from the early days of personal computing all the way to today's modern enterprise systems. These days, I'm all about:

• Systems Engineering and Administration (because someone's got to keep the lights on)
• Cloud Technologies (with a soft spot for Azure)
• Virtualization and Automation (because who likes doing things manually?)
• AI and Machine Learning integration in IT operations (it's not skynet, I promise)

I've got this itch for continuous learning and innovation, which is pretty much why I kicked off this project. I wanted to dive into the crazy intersection of AI, DevOps, and home lab environments. It's my way of mashing together years of industry experience with cutting-edge tech to create something unique. Think of it as a playground for learning and experimenting – only instead of swings and slides, we've got servers and AI models.

🤝 Contributing

We welcome contributions from the community. Please read our CONTRIBUTING.md file for guidelines on how to make contributions.

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

📬 Contact

Project maintained by VintageDon.

For any queries, please open an issue in this repository.

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Note: This README is a living document and will be updated as the project evolves. Last updated: [DATE]

💸 Sponsors

We'd like to thank our sponsor for supporting this project:

MSP4 LLC - Best in class IT support & consulting

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Happy learning, and enjoy building your Proxmox home lab!

📖 Wiki

For detailed information about the project, including current lab stats, infrastructure diagrams, and the latest VM lists, please visit our Wiki. The Wiki is regularly updated with the most current information about the lab environment and project progress.

🙏 Acknowledgments

Lot of great projects that inspired or helped.

• BASH Scripting Boilerplate: Bashew
• Self-Hosted App Master List: Awesome Self Hosted