eurybia VS TensorFlow-Examples

Try out eurybia, from the author of shapash which is a brilliant library as well.

Your data scientists will cook up ugly bits of code to prepare their training data, you'll probably have to rewrite that when they want to ship to prod and also detect and handle discrepancies. In that regard, it sounds like you may enjoy Eurybia to communicate about this data with your data scientists. We made it precisely for that.

So we just trained a model to try and do the same, and then sort of read its entrails through Shapash. The more it can tell the difference, the more your data has changed. We can know which variable has changed the most, and how much it's important to our models. If all else fails (and also if all else works), we can still know (again, this is all quantified in some way, we need numbers, not eyeballings) how much our models predictions have evolved over time, independantly of particular data changes, legit or not. How can our models predictions change if the data is all clean, you ask ? I mean I asked, but you would have too, in my shoes. What lies beyond data engineering ? What is the meaning of life ? The answer is concept drift, and that's where we're starting to work on now that we have a good grasp on data drift. Anyways, the tool is Eurybia. If any part of my ramblings resemble some of your work, please give it a try and chat us up here or through the repo, we are of course very eager to get feedbacks and possibly even contributions, who knows. See ya !

A linear regression model

When I run this simple project, the log output is below. There is a 5-minute pause at 16:48. There is a second pause at the end of the script before the output of the example (final output excluded). This project runs quickly if I exclude "--gpu" and run it on the CPU.