I have a situation where my data can only be read from in a hosted Python environment, due to data security reasons. However, I am constrained to run ML models in a Rust environment due to work-related factors such as the expertise of the team. Does it make sense to do ML model training in scikit-learn, and then use those parameters in Rust. I have 2 types of models I need to use: logistic regression and SVM.
Based on the documentation for both scikit-learn and linfa, I think I can simply return the trained parameters, then manually copy over the parameters into my rust system and run inference using linfa. I want to know if anyone else has faced this problem before, and whether there could be a more streamlined way to do this?
Proposal: Derive optimal model parameters in scikit-learn > Save these parameters in a file > Read these parameters into Rust code > Initialize
linfa model using parameters > Store
From what I understand, there might be a problem in storing and loading
linfa models: https://github.com/rust-ml/linfa/issues/67
Would be keen to hear from people's experience on this.