.NET AI architecture and DevOps [Austin .NET UG]

Challenges

  • Integrating AI capabilities into existing .NET applications effectively
  • Understanding where large language models fit within software architecture
  • Adapting DevOps workflows to support AI-driven development, testing, and deployment

Solutions

  • Architectural guidance on embedding AI into .NET applications
  • Best practices for integrating large language models into existing systems
  • Strategies for incorporating AI into DevOps processes, including source control, builds, and testing

Benefits

  • Seamless AI integration into enterprise .NET applications
  • Optimized DevOps pipelines that support AI-driven workflows
  • Increased efficiency in deploying and managing AI-powered software

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.

Join Jeffrey Palermo, Chief Architect of Clear Measure, a software architecture company that empowers our client’s development teams to be self-sufficient: Moving fast, delivering quality, and running their systems with confidence.