File-Based Apps in .NET 10: C# Without the Ceremony
Build a URL uptime checker in a single C# file with .NET 10 file-based apps. No project or solution needed, plus NuGet packages.
Jon Wood
Tutorials and ideas.
How to Handle Big Data Files in C# with Parquet.NET
Read and write Parquet files in C# with Parquet.NET, and see why Parquet beats CSV for large datasets. Includes a complete example.
How the Machine Learning Process is Like Cooking
How the machine learning process, from data to a deployed model, mirrors cooking a meal, and why following the process leads to better models.
ML.NET Predictions on the Web in F# with the SAFE Stack
Build an F# web app with the SAFE Stack and serve ML.NET predictions from it, using F# on both the server and the client.
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File-Based Apps in .NET 10: C# Without the Ceremony
Build a URL uptime checker in a single C# file with .NET 10 file-based apps. No project or solution needed, plus NuGet packages.
How to Handle Big Data Files in C# with Parquet.NET
Read and write Parquet files in C# with Parquet.NET, and see why Parquet beats CSV for large datasets. Includes a complete example.
What's New in the ML.NET CLI
What's new in the ML.NET CLI, including the new platform-specific install names and other updates.
Introduction to QnA Maker
Turn an FAQ page into a chatbot knowledge base with Microsoft QnA Maker. Note: QnA Maker is retired in favor of Azure AI Language question answering.
Use Bing Image Search to Get Training Image Data
Use the Bing Image Search API to download training images for an image classification model. Note: the Bing Search APIs were retired in 2025.
The ML.NET Deep Learning Plans
The ML.NET team's plan for deep learning: what's supported today and the roadmap for training neural networks from scratch in ML.NET.
AI Ethics and Fairness Resources
Videos and research papers on AI ethics and fairness, covering how bias gets into data and models and how to reduce it.
What's New in ML.NET Version 1.6
Highlights of the ML.NET 1.6 release, with links to the pull requests behind each new feature. No breaking changes.