How do you organize your Machine Learning model's components?

Pre-Coding Prep: Get ready before coding begins. Problem & Goals: Know what issue you're solving and what you want from your model. Model Purpose: Figure out why you're using machine learning and what it'll do. Inputs & Outputs: List what goes in (data) and what comes out (predictions/results). Success Measures: Define how to tell if your model is doing well (metrics). Evaluation Criteria: Decide how to check if your model works as planned. Deployment & Monitoring: Plan how the model will go live and how you'll watch over it.

- I agree, having a clear problem statement is paramount and it’s also important to differentiate between solutions expected and what the model would actually do. Having metrics aligned with model expectations is critical to define along the problem statement

First you need to identify if the problem at hand can be solved by a machine learning model and if you have data that is representative to allow you to build a good model.

If ou have time, look for some papers related to the issue and try to start from a common and already known solution before making up your own.

In my experience, one of the worst things to deal with is unorganized jupyter notebooks. Try make the notebook sections concise, small and with just the right amount of code needed to make it run without error. In case of deployeing a model, follow the team project architecture, if its a pipeline and you have a cascating model, try to separate each stage in the pipeline correctly, maybe APIs for each stage of the pipeline would be a good call.

I'd agree with the importance of modularizing code! I've found it's pretty important to have distinct blocks within the model. For example, I'll first get the data into a usable table, then create the model (including the sub-steps within it), and then finally put the key findings at the end of it. Visuals are a key part of the end presentation, but also many other steps along the way too! If I'm building a model, I'll often put everything together in one file separated by descriptions (a great example of how to use markdown in notebooks to explain a lot of the process). For production though, I'll try to separate it cleanly into different files.

Creating unit tests or integration tests are very good option as a sanity checker, making sure every aspect of the solution are receiving and outputing the correct type of data and raising errors as well.

Training model, capturing and comparing results is a big part of organizing your ML models, training and evaluating experiments.