{"id":1729,"date":"2026-08-07T19:19:24","date_gmt":"2026-08-07T19:19:24","guid":{"rendered":"https:\/\/vykhodtsev.dev\/?p=1729"},"modified":"2026-08-07T19:19:24","modified_gmt":"2026-08-07T19:19:24","slug":"%f0%9f%9a%80-migrating-ai-data-lab-from-my-laptop-to-microsoft-azure","status":"publish","type":"post","link":"https:\/\/vykhodtsev.dev\/?p=1729","title":{"rendered":"\ud83d\ude80 Migrating AI Data Lab from My Laptop to Microsoft Azure"},"content":{"rendered":"\n

August 2 2026 \u2022 Infrastructure Update<\/em><\/p>\n\n\n\n

When I started AI Data Lab<\/strong>, everything ran directly on my laptop. It was the fastest way to prototype ideas, experiment with AI, and iterate quickly. As the project evolved into a real platform with multiple services, web interfaces, and cloud AI integration, it became clear that the next step was moving to Microsoft Azure.<\/p>\n\n\n\n

This migration was not only about hosting the application online\u2014it was about building a scalable foundation for future AI agents and enterprise-grade features.<\/p>\n\n\n\n

\n\n\n\n

Why move to Azure?<\/h2>\n\n\n\n

Running everything on a local machine worked well during early development, but it also introduced several limitations:<\/p>\n\n\n\n

    \n
  • The application was only available while my laptop was powered on.<\/li>\n\n\n\n
  • Local networking complicated remote access and testing.<\/li>\n\n\n\n
  • Database backups depended on manual processes.<\/li>\n\n\n\n
  • Scaling the application for multiple users wasn’t practical.<\/li>\n\n\n\n
  • Integrating additional cloud services required extra infrastructure.<\/li>\n<\/ul>\n\n\n\n

    Moving to Azure solved these problems while preparing the project for future growth.<\/p>\n\n\n\n

    \n\n\n\n

    New Cloud Architecture<\/h2>\n\n\n\n

    The current platform is built around Azure services that separate application logic, storage, databases, and AI capabilities.<\/p>\n\n\n\n

    Core components<\/h3>\n\n\n\n
      \n
    • Azure App Service<\/strong>\n
        \n
      • Hosts the FastAPI backend<\/li>\n\n\n\n
      • Automatically manages the web server<\/li>\n\n\n\n
      • Supports continuous deployment from GitHub<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • Azure Database for MySQL<\/strong>\n
          \n
        • Replaces the local MySQL instance<\/li>\n\n\n\n
        • Automatic backups<\/li>\n\n\n\n
        • Built-in high availability<\/li>\n\n\n\n
        • Managed updates and maintenance<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Azure OpenAI<\/strong>\n
            \n
          • Natural language processing<\/li>\n\n\n\n
          • SQL generation<\/li>\n\n\n\n
          • AI reasoning<\/li>\n\n\n\n
          • Future AI agents<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Azure Blob Storage<\/strong>\n
              \n
            • File uploads<\/li>\n\n\n\n
            • Generated reports<\/li>\n\n\n\n
            • Future document processing<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • GitHub Actions<\/strong>\n
                \n
              • Automatic deployment<\/li>\n\n\n\n
              • CI\/CD pipeline<\/li>\n\n\n\n
              • Version-controlled releases<\/li>\n<\/ul>\n<\/li>\n\n\n\n
              • Custom Domain & HTTPS<\/strong>\n
                  \n
                • Secure access through vykhodtsev.dev<\/strong><\/li>\n\n\n\n
                • SSL certificate managed by Azure<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

                  August 2 2026 \u2022 Infrastructure Update When I started AI Data Lab, everything ran directly on my laptop. It was the fastest way to prototype ideas, experiment with AI, and iterate quickly. As the project evolved into a real platform with multiple services, web interfaces, and cloud AI integration, it became clear that the next 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