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For .NET Developers

Learn .NET with AI —Build Intelligent Applications

Specifically designed for C# and .NET developers. Learn to integrate AI capabilities into your existing .NET apps using Semantic Kernel, ML.NET, Azure OpenAI SDK, and Microsoft AI frameworks.

45Hours
8Modules
6+Projects
C#Language
Prerequisites

C# and .NET Core/6+ knowledge

Basic understanding of REST APIs

Visual Studio or VS Code

No prior AI/ML experience required

Tools & Technologies
🔷 Semantic Kernel 🤖 ML.NET ☁ Azure OpenAI SDK ⚡ .NET 8/9 🌐 ASP.NET Core API 🔥 Blazor 📦 ONNX Runtime 🐳 Docker ☁ Azure AI Search 🗃 Qdrant

📋 Detailed Course Syllabus

Module 1: Azure OpenAI with .NET SDK 6 Hours Beginner
  • Setting up Azure OpenAI Service —resource creation, deployment, API keys
  • Azure OpenAI .NET SDK (Azure.AI.OpenAI NuGet) —installation and configuration
  • Chat completions in C# —system messages, user messages, streaming
  • Managing API keys with .NET User Secrets and Azure Key Vault
  • Embeddings in C# —generating vectors with OpenAIEmbeddingClient
  • Image generation with DALL-E 3 using .NET SDK
  • Dependency injection setup for OpenAI clients in ASP.NET Core
Module 2: Semantic Kernel Deep Dive 8 Hours Intermediate
  • Semantic Kernel architecture: Kernel, Plugins, Memory, Planners
  • Creating your first Kernel with OpenAI/Azure OpenAI connector
  • Semantic functions (prompt-based) —inline and from files
  • Native functions (C# code) —creating and registering Kernel plugins
  • KernelArguments and parameterized prompts
  • Auto-function invocation —let the AI decide which C# functions to call
  • Chat history management and conversation context
  • Process Framework —building multi-step workflows with events and steps
  • Project: AI-powered task manager that uses C# functions to manage data
Module 3: Semantic Memory & RAG with .NET 7 Hours Intermediate
  • Vector stores in .NET: Semantic Kernel VectorStore abstraction
  • Integrating with Qdrant, Azure AI Search, In-Memory stores
  • Chunking documents in C#: splitting PDFs and Word files (DocumentFormat.OpenXml, PdfPig)
  • Building a RAG pipeline entirely in C# with Semantic Kernel
  • Text search vs. Vector search —when to use each
  • Hybrid search: combining keyword + semantic search with Azure AI Search
  • ITextSearch and VectorStore integration patterns
  • Project: Corporate document Q&A API built with ASP.NET Core + Semantic Kernel
Module 4: ML.NET —Machine Learning in .NET 6 Hours Intermediate
  • ML.NET overview —training and consuming ML models in .NET without Python
  • ML.NET data pipeline: IDataView, transformations, feature engineering
  • Binary classification: spam detection, sentiment analysis
  • Regression: price prediction models
  • Recommendation: product recommendations with Matrix Factorization
  • Image classification with ML.NET and ONNX model integration
  • Model Builder (Visual Studio extension) —AutoML for .NET developers
  • Consuming ONNX and PyTorch models in .NET with ONNX Runtime
Module 5: Building AI-Powered ASP.NET Core APIs 6 Hours Intermediate
  • Designing RESTful AI APIs —chat, completion, search, image generation endpoints
  • Streaming SSE (Server-Sent Events) from ASP.NET Core for real-time AI responses
  • Rate limiting and throttling for AI API endpoints
  • Caching AI responses with IDistributedCache (Redis)
  • Middleware for prompt logging and response monitoring
  • Authentication and authorization for AI endpoints (JWT + Azure AD)
  • Swagger/OpenAPI documentation for AI-powered APIs
  • Project: Build and deploy a multi-modal AI API (text + image) with ASP.NET Core
Module 6: Blazor AI Chat Applications 5 Hours Intermediate
  • Building a ChatGPT-like UI with Blazor Server and Blazor WebAssembly
  • Real-time streaming chat UI using SignalR + IAsyncEnumerable
  • Markdown rendering for AI responses in Blazor (Markdig library)
  • File upload and document chat UI in Blazor
  • State management for chat history in Blazor components
  • Deploying Blazor AI apps to Azure App Service with CI/CD
Module 7: .NET AI Agents with Semantic Kernel 4 Hours Advanced
  • ChatCompletionAgent —building conversational agents in C#
  • OpenAIAssistantAgent —using OpenAI Assistants from .NET
  • Agent collaboration: AgentGroupChat for multi-agent orchestration
  • Termination strategies: keyword-based, function-based, max-iteration
  • Integrating agents with ASP.NET Core Web API endpoints
Module 8: Production Deployment & Capstone 3 Hours Advanced
  • Docker containerization of .NET AI applications
  • Azure Container Apps deployment with auto-scaling
  • Application Insights integration for AI token usage monitoring
  • Responsible AI: content filtering with Azure AI Content Safety SDK
  • Capstone Project: Full-stack AI application —ASP.NET Core API + Blazor frontend + Semantic Kernel RAG + Azure OpenAI + deployed to Azure

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