One of both the benefits and drawbacks of the CastMinder system is its tendency to produce data. A lot of data. One sensor log per second from up to fifteen sensors means thousands of sensor logs an hour. One of the questions I was faced with while designing CastMinder was how can I use this data to help detect complications?

I've developed a few solutions. Lately, I have been experimenting with a great GitHub library called Swift AI that makes it easy to add Machine Learning to iOS apps. I can use the GPU-accelerated algorithm to parse thousands and thousands of data points, then develop correlations between them to predict future cast conditions.