Hey everyone, I'm trying to understand how experienced folks actually work in practice — not just the modeling side, but the system design and documentation side.
One thing I've been struggling to find good examples of is how teams document their ML architecture. Like, when you're building a training pipeline, a RAG system, or a batch scoring setup — do you actually maintain architecture diagrams? If so, how do you create and keep them updated?
A few specific things I'm curious about:
- Do you use any tools for architecture diagrams, or is it mostly hand-drawn / draw.io / Miro?
- How do you describe the components of your system to a new team member — is there a doc, a diagram, or just verbal explanation?
- What does your typical ML system look like at a high level? (e.g. what components are almost always present regardless of the project?)
- Is documentation something your team actively maintains, or does it usually fall behind?
I know a lot of ML content online focuses on model performance and training, but I'm trying to get a realistic picture of how the engineering and documentation side actually works at teams of different sizes.
Any war stories, workflows, or tools you swear by would be super helpful. Thanks!
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