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AIOps — anomaly detection and an LLM runbook assistant
In build — target Sept 13–14, 2026. The plan below is complete; the working detector, assistant transcripts and screenshots land here as the build finishes.
Python anomaly detection LLM API RAG
What this project builds
Two parts. Part A — unsupervised anomaly detection over log-derived metrics, running locally: extract metrics from operational logs, train a compact model, flag the anomalies, plot the results. Part B — a retrieval-augmented assistant that answers "what do I do about this alert?" from my own runbooks, with citations.
How it will be built
- Part A in plain Python: generate_sample_logs.py → detect_anomalies.py → plot_results.py; the detection model itself is a handful of lines — the craft is in the feature extraction.
- Part B: my real runbooks embedded and retrieved, an LLM API composing grounded answers that cite the runbook they came from. Under USD 5 total in API calls.
Why it matters
Most AIOps demos have to invent their domain. I have the ingredient that can't be faked: years of real operational logs and real runbooks from production systems. This project connects that operational depth to the AI tooling wave — the combination the market is starting to hire for.