Opleiding: Building .NET AI Agents and Apps
The course Building dotNET Agents and Apps from SpiralTrain teaches you how to develop AI-powered applications and agents using modern .Intro to AI and .NET
Covers the AI landscape within .NET: ML.NET, ONNX, Azure AI, LLMs, Copilot, and .NET 8 features. Learn core terms like inference, agents, and prompts and explore practical use cases.
Smart .NET Apps with ML.NET
Walks through creating ML.NET pipelines, training models, and deploying them via web APIs. Learn evaluation, feature engineering, and how to integrate models into real .NET apps.
OpenAI & Azure AI in .NET
Connect to OpenAI and Azure Cognitive Services via C#. Create intelligent agents, chat assistants, and analyze costs and streaming responses. Learn secure integration practices.
AI Agents & Semantic Kernel
Learn to create AI agents using Microsoft's Semantic Kernel. Understand plugins, memory, skills, planning, and integrate external APIs like calendar/weather into goal-driven agents.
Prompt Engineering & NL Interfaces
Focus on prompt crafting and templating, vector search, embeddings, and building RAG-based solutions in .NET. Learn how to handle hallucinations and design interactive NL interfaces.
Deploying .NET AI Applications
Build and deploy full AI solutions with Blazor or ASP.NET Core. Use SignalR for real-time feedback, manage long tasks, and deploy with Azure. Final project ties it all together.
Audience Course Building .NET Agents and Apps
This course is intended for .NET developers, architects, and AI enthusiasts who want to integrate intelligent features and agents into modern .NET applications.
Prerequisites Course Building .NET Agents and Apps
Participants should have a basic knowledge of C# and .NET development. Familiarity with APIs, Visual Studio, and basic AI concepts will be helpful.
Realization Training Building .NET Agents and Apps
The course combines theoretical sessions with hands-on labs guided by an expert trainer. Practical exercises and real-world applications are central to the training experience.
Building .NET Agents and Apps Certificate
After completion, participants receive a certificate of participation in Building .NET Agents and Apps.
Modules
Module 1: Intro to AI and .NET
- Overview of AI/ML in .NET
- Key concepts: Models, Inference, Agents
- ML.NET, Azure AI, ONNX
- LLMs and modern app development
- Cognitive services and APIs
- Cloud vs local AI models
- .NET 8 AI features
- Setting up environment
- Copilot in .NET productivity
- Use cases in .NET AI
Module 2: Smart .NET Apps ML.NET
- Intro to ML.NET
- Building a sentiment model
- Data processing and features
- Using Model Builder
- Saving/loading models
- Evaluation and tuning
- ML in ASP.NET
- Deploying prediction APIs
- Production model usage
- Model integration patterns
Module 3: OpenAI and Azure AI
- OpenAI API in .NET
- Azure OpenAI differences
- API key setup & auth
- First GPT request in C#
- Creating chat assistant
- Token cost management
- Streaming responses
- Vision/Speech/Language APIs
- Azure Translator in apps
- Building Azure + OpenAI bots
Module 4: AI Agents Tooling
- What is an AI Agent?
- LangChain vs Semantic Kernel
- Semantic Kernel SDK intro
- First AI agent in .NET
- Plugins & skills in SK
- Planning strategies
- Memory and context
- External API integration
- Logging and debugging
- Use case: Calendar agent
Module 5: Prompt Engineering
- Prompt engineering basics
- Templated prompts in SK
- Chaining prompts
- Managing conversation history
- Prompt tips for .NET devs
- Vector search with embeddings
- Using Pinecone, Redis, AI Search
- RAG pattern implementation
- Use case: Doc Q&A system
- Fallback and hallucination handling
Module 6: Deploying .NET AI Apps
- Building full-stack AI apps
- Blazor vs ASP.NET AI UIs
- Secure HTTP APIs
- SignalR for live AI updates
- Caching/throttling responses
- Long-running workflow handling
- Azure App Service & Containers
- Testing strategies & logging
- NuGet packaging of agents
- Final project: AI assistant demo
