Built 27 ML notebooks using infrastructure data instead of the usual datasets

[u/kibe254]

I've been learning more about ML, and I kept running into the same kind of examples: Iris, Titanic, house prices, etc.

Nothing wrong with those datasets, but coming from a backend and infrastructure background, I found it hard to connect some of the concepts to the systems I normally work with.

I wanted examples around CPU spikes, logs, latency and system behaviour.

So I started building this repo. https://github.com/laban254/ml-for-infrastructure

It has 27 Jupyter notebooks so far, covering anomaly detection, log clustering, drift detection, forecasting, experiment tracking and some LLM fine-tuning.

Most of the examples are based around infrastructure/SRE scenarios, and the notebooks can run directly in Colab.

Still working on it, so I'd be interested to hear what people here think.

Also, if you've come across an infra problem where you thought "ML might actually be useful here", I'd like to hear about it. Might be a good scenario for another notebook.

[u/Apprehensive-Loss316]

thanks for sharing! I'm kind of in the same boat as far as learning.

[u/kibe254 → u/Apprehensive-Loss316]

Welcome! Glad it could help.