AI + Machine Learning for .NET Developers
summary
Shows concrete, working integrations: uses Azure Cognitive Services Computer Vision then swaps to Custom Vision offline by loading a exported model.pb so the app correctly classifies a frisbee that the generic model misses. Builds and trains two ML.NET models (product and country sales) in a console app, exports ZIP models and loads them into the web UI to render forecast charts. Uses eShopOnContainersAI microservice sample with Docker, Bot Framework + LUIS image search, ML.NET (v0.5) TensorFlow interop and edge scoring for IoT.
AI-written summary · about
chapters (8)
- 0:00Start
- 4:49Demo Using Cognitive Services
- 13:16Pre-built ML Models (Azure Cognitive Services)
- 13:36Build your own (custom) ML Models
- 15:30ML.NET is a framework for custom ML
- 23:44The Goal for ML.NET ?
- 31:52Azure Stream Analytics
- 34:11Resources
related talks
57mEasier AI allows everyone to build smart apps
53mBuilding Intelligent .NET MAUI Apps with ML.NET
01h 02mAI on the Edge
44mAI Building Blocks for .NET: Add intelligence to your C# Apps
01h 14mAI of Things
44mAI for every developer
58mExploring Machine Learning for .Net Developers
46mInfusing your .NET Apps with AI: Practical Tools and Techniques