.NET AI Architecture and DevOps—Memphis Global AI Community Bootcamp
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.
