What would make an ML curriculum for SREs actually useful day-to-day?
I got tired of ML tutorials that teach through flowers and passenger manifests.
https://github.com/laban254/ml-for-infrastructure
As someone who spends time looking at dashboards, digging through log files, and getting paged at bad hours, I wanted to learn ML through problems I actually face, not toy datasets. So over the past few months, I put together a curriculum of 27 Jupyter notebooks, all framed around real observability and SRE scenarios.
A few examples: Isolation Forest anomaly detection on synthetic Prometheus metrics with real daily seasonality (with a slider to see how the contamination parameter changes alert volume, and a Z-score comparison to show why static thresholds miss seasonal anomalies). Log clustering with TF-IDF + KMeans that auto-names clusters from keywords and flags novel patterns it hasn't seen before. KS-test drift detection for when a production distribution has permanently shifted. A PyTorch LSTM that does recursive forecasting with a preemptive capacity alert. MLflow tracking for a full hyperparameter sweep with inline run comparison. And a small LoRA fine-tune that turns raw log lines into structured JSON.
Genuinely curious what people who actually do this job think: what production scenarios am I missing that would be worth adding? Does this kind of framing (real infra data instead of toy datasets) actually help build intuition, or is it a gimmick?