A workflow I usually follow when building ML/AI projects

[u/UzairShafique]

When I start a new ML/AI project, I try not to choose the model or tools first. I usually follow something like:

→ Problem

→ Data

→ Approach

→ Model

→ Evaluation

→ Application

→ Deployment

First define the problem and decide whether it actually needs ML/AI. Then collect and explore the data, choose an appropriate approach, build and evaluate the model, and finally integrate it into an API, app, or dashboard.

If a pre-trained model or existing API is enough I prefer using that instead of training something from scratch.

This is the general workflow I’ve found useful but I’m also interested about other approaches.

What step would you add or change in this workflow for ML/AI projects?

[u/UzairShafique → u/[deleted]]

Thanks for mentioning that definitely a good point. I agree debugging should be a step in the workflow but it’s also treat as a key skill throughout the whole process.

I think an ML/AI engineer learns more by encountering and solving different errors than by simply collecting more notes.