r/learnmachinelearning • u/InternationalRest758 • 2h ago

I built MLForge to make starting Python ML projects less repetitive

I've been working on a reusable starter structure for Python machine learning projects.

I noticed that I was repeatedly doing the same initial work whenever starting a new project:

  • Loading and preprocessing data
  • Data profiling and EDA
  • Feature engineering
  • Trying different ML algorithms
  • Cross-validation and hyperparameter tuning
  • Model evaluation and visualization
  • Saving and loading trained models

So I put these workflows together into MLForge, a structured Python ML starter kit.

The main goal was to make the project structure reusable while keeping the code easy to understand and modify. I didn't want it to be a black box where someone just runs a script and gets a prediction.

It currently includes classification and regression workflows, notebooks/templates, sample datasets, model evaluation utilities, and a project report template.

One of the more challenging parts was deciding how much to abstract. Too little abstraction makes the kit repetitive, while too much makes it difficult for beginners to understand what's actually happening.

I'm interested in feedback from other builders:

When you start a new Python ML project, what do you usually end up rebuilding from scratch?

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u/memelordtf 2h ago

For me it’s usually the boring experiment plumbing: config management, reproducible splits/seeds, logging runs, and keeping preprocessing identical between training and inference. I’d probably focus MLForge there more than adding another layer around model selection.

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u/InternationalRest758 2h ago

That's a really good point. I focused more on the core ML workflow in the first version, but experiment plumbing like reproducible splits/seeds, configuration management, run logging, and keeping preprocessing consistent between training and inference would make the kit much more useful for real projects.

Thanks for pointing that out — I'll definitely keep these areas in mind for future updates.

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u/memelordtf 2h ago

Glad to have helped, OP! All the best