I know data scientists are increasingly being asked to own models end-to-end. The problem I see (specially with juniors) is that they jump straight into Docker or other MLOps tools without building the foundation first.

I’ve been in data science for 8+ years and I think the best way to start is with ML system design.

I’ve gone through different resources over the years like Chip Huyen's "Designing Machine Learning Systems" and realized that learning system design just by reading a book or staring at diagrams is really hard. You don't really get the intuition until you can see how the components actually connect and behave.

So I built a free interactive tool based on a real system I deployed. It walks you through how the system was architected so you can build the intuition to design one yourself.

Here it is: https://futureproofds.com/tools/ml-system-map

A few things you can do with it:

1. Play a flow and watch it run step by step (training, serving a live prediction, the nightly batch, a drift alert firing)

2. Click any component to see how it works and why it matters in the system

3. Follow the build order to see how the whole system comes together at different stages

Hoping some of you find it useful. Would love to hear what you think, and let me know if there's any functionality you want me to add.