It has been awhile since I posted about Anomaly detection using F# and ML.NET. Since the ML.NET framework continues to evolve, so it is worth a revisit to investigate changes. This also provides a good opportunity to dig deeper into the anomaly detection options that are provided.

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Persisting data can be a subtle art. Today I am taking a look at how F#’s Discriminated Unions can interact with Dapper. I’ve discussed Dapper before, but never really discussed its facilities for interacting with Discriminated Unions. It is a useful bit of knowledge when determining project data structures.

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Today is again a lighter post playing with visualizations. The data focus is on the Standardized Precipitation Index data for the U.S. over that last one-hundred years. Static images and data can be useful, but visualizing data over time can be a welcome addition for analysis. So I’ll be converting data ultimately into a video of the data over time using primarily F#.

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Sometimes an application has a need for a local key/value store. In these scenarios, there are several options, including RocksDB. Today’s exploration will be to dig into using RocksDB with F#.

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Text search is something we do everyday. Fast and reliable search is such a staple, it is easy to forget there can be elegance to those underlying mechanisms. It is time to pull back the curtain and dig into one of the foundational search algorithms, the Boyer-Moore fast string search. It is a good algorithm to demonstrate some of the methods used to achieve fast search results. As typical, I’ll reach for F# to show implementation details.

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