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#dotnet #machinelearning #workskills #python #ai 

As a .NET developer, do you find yourself at a crossroads when venturing into the world of machine learning and AI? Find yourself reaching for notebooks and scripts in another language? For at least the last decade, Python has been the language of choice for data scientists and machine learning engineers everywhere, thanks to the wonderful world of Jupyter Notebooks. It’s been pretty rare to find an ML pipeline using .NET in the wild, especially since the limited support within cloud services like AWS Sagemaker.

In the last few years however, .NET finally caught up to the world of notebooks, in fact it might even be starting to overtake it. In this session we’ll examine how .NET notebooks work, how you can use them as part of your codebase, and how to build modern day ML pipelines in the cloud entirely in .NET!

NDC Conferences

A conference talk exploring how .NET developers can use Jupyter and Polyglot notebooks for interactive development, documentation, runbooks, data science, and ML/AI workflows. The speaker argues that .NET has not only caught up with Python's notebook ecosystem but extended it through Polyglot Notebooks, which support multi-language cells (C#, F#, PowerShell, Python) with shared variables. Practical demos cover using notebooks as sandboxes for library documentation, teaching concurrency concepts, disaster recovery runbooks with AWS LocalStack, data analysis with Microsoft's DataFrame library and Plotly.NET, and training linear regression models with ML.NET. The talk acknowledges Python's dominant market position but makes the case that .NET-centric teams can viably consolidate their ML/AI work within the .NET ecosystem, or use both languages side-by-side in the same notebook.

Notebooks & .NET: A Marriage Made In Jupyter - Alexander Hall - NDC London 2026