My latest personal project, a personal finance tracker with ML-powered insights, started with a simple feature to categorize expense but quickly got expanded to accommodate multiple features including handling everything from transaction classification to spending predictions (I was greedy to get into ML based investment recommendations but oh boy I don’t think I’m there yet to believe in making ML recommended investments :D). When one model failed, everything failed.
So I decided to do what I’d been putting off for months: break the monolith apart. Here’s what I learned decomposing my personal ML project into focused microservices, and why, I think, you might want to consider the same approach for your own projects.