Azure AI services offer robust tools and platforms that enable developers to bring their AI solutions from concept to production seamlessly. Using .NET 8 alongside these services, developers can experiment, build, and scale their AI applications effectively. This post explores how you can harness the power of Azure AI and .NET to transform your ideas into production-ready AI solutions.

From Prototyping to Production with Azure AI

Start your AI journey by experimenting with local prototypes using Azure AI’s extensive suite of tools. Azure Machine Learning and Azure Cognitive Services provide the necessary components to plug in different AI models and build comprehensive solutions. When you’re ready to scale, Azure OpenAI Service and .NET Aspire enable you to run and monitor your applications efficiently, ensuring high performance and reliability.

Why Build AI Apps with Azure AI Services?

Integrating AI into your applications with Azure AI offers numerous benefits:

Enhanced User Engagement: Deliver more relevant and satisfying user interactions.
Increased Productivity: Automate tasks to save time and reduce errors.
New Business Opportunities: Create innovative, value-added services.
Competitive Advantage: Stay ahead of market trends with cutting-edge AI capabilities.

Getting Started with Azure AI and .NET

Explore the new Azure AI and .NET documentation to learn core AI development concepts. These resources include quickstart guides to help you get hands-on experience with code and start building your AI applications.

Utilizing Semantic Kernel

Semantic Kernel, an open-source SDK, simplifies building AI solutions by enabling easy integration with various models like OpenAI, Azure OpenAI, and Hugging Face. It supports connections to popular vector stores such as Weaviate, Pinecone, and Azure AI Search. By providing common abstractions for dependency injection in .NET, Semantic Kernel allows you to experiment and iterate on your apps with minimal code impact.

Testing and Monitoring with .NET Aspire

.NET Aspire offers robust support for debugging and diagnostics, leveraging the .NET OpenTelemetry SDK. It simplifies the configuration of logging, tracing, and metrics, making it easy to monitor your applications. Azure Monitor and Prometheus can be used to keep an eye on your production deployments, ensuring your applications run smoothly.

Real-World Example: H&R Block’s AI Tax Assistant

H&R Block has developed an innovative AI Tax Assistant using .NET and Azure OpenAI, transforming how clients handle tax-related queries. This assistant provides personalized advice and simplifies the tax process, showcasing the capabilities of Azure AI in building scalable, AI-driven solutions. This project serves as an inspiring example for developers looking to integrate AI into their applications.

Join H&R Block at Microsoft Build as they discuss their journey and experience building AI with .NET and Azure in the session, Infusing your .NET Apps with AI: Practical Tools and Techniques.

Learn More

To dive deeper into AI development with Azure AI and .NET:

Explore the latest .NET and Azure AI documentation
Get started with our quickstart guides for Azure AI and Semantic Kernel
Read the Semantic Kernel announcement post
Share your feedback and connect with our team

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