Data science and machine learning are hot topics, but not everybody can drop everything and jump over to the R or Python ecosystems. If you work in a .NET shop, you likely have years of accumulated code and systems which your customers already use, and you might not be able to move to a microservices architecture for integrating code written in other languages. Making a clean break may be out of the question, but we can still use familiar languages and techniques to introduce data science and machine learning to the workplace. In this talk, we will build machine learning models within C# and then see how we can use F# to take our code to the next level while remaining fundamentally compatible with an existing code base. No F# experience is required, although some familiarity with the language would be helpful.


The slides are available as a GitPitch slide deck.

The slides are licensed under Creative Commons Attribution-ShareAlike.

Demo Code

The demonstration code is available on my GitHub repository. This includes a set of .NET projects.

The source code is licensed under the terms offered by the GPL. The slides are licensed under Creative Commons Attribution-ShareAlike.

Links and Further Information

This talk looks suspiciously similar to my Launching a Data Science Project talk. There's a fair amount of overlap, particularly on the process slides, but if you want a deeper dive into a more realistic project than the Bills winning football games, check out the accompanying notebook as well as links to cool projects like MariFlow and MarIO.


Additional Resources

F# and ML