.NET AI Architecture and DevOps

Challenges

  • Integrating AI without disrupting existing workflows
  • Deploying models alongside traditional code
  • Testing AI components within CI/CD pipelines

Solutions

  • Architecture patterns for AI in .NET
  • DevOps integration of AI-driven features
  • Managing AI models in source control and builds

Benefits

  • AI features embedded into production systems
  • Scalable and maintainable AI architecture
  • Seamless DevOps processes for AI-enabled apps

While it is useful and necessary to learn and use AI tools, .NET developers need to be able to integrate AI into existing software applications.

This session will focus on the architecture implications of AI, where large language models fit into existing .NET applications, and how to integrate them into DevOps environments, including source control, builds, testing, and deployment.