Notebooks & .NET: A Marriage Made In Jupyter
summary
Explains how to install the global tool dotnet-interactive and use the .NET kernel inside polyglot notebooks so cells can run C#, F#, PowerShell and share variables with Python; shows building sandboxes (demoed with an open source sanitizer “soap on a scope”), interactive teaching examples for concurrency, runbooks for disaster recovery that query SQL and S3, and data analysis with Microsoft DataFrame + plotly.net on an NHL CSV. Demonstrates training and saving a model with ML.NET linear regression (R² ≈ 0.98) and notes SageMaker / VS Code integrations.
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chapters (11)
- 0:00Welcome
- 7:34Python dominance in AI/ML
- 17:08Notebooks make .NET interactive
- 26:43Sandboxes for testing/docs
- 31:53Teaching with notebooks
- 36:43Runbooks for recovery/automation
- 44:06Data science in .NET notebooks
- 47:55Polyglot notebooks support
- 51:49ML/AI in .NET notebooks
- 55:21Python vs .NET for ML trade-offs
- 57:47Closing & Q&A
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