AI Anomaly Detection

6 Interactive Sessions

Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.

1. 1

   The Signal in the Noise — shaping data you can trust

   Before you can catch the spike, four data sources have to reconcile into one clean, time-aligned, windowed stream — because you cannot detect what you never collected cleanly.

2. 2

   What's Normal? — the baseline that breathes

   Teach the system what normal looks like — a band that moves with the daily and weekly rhythm — so a value is judged against what it should be at THIS hour, not a flat average.

3. 3

   When Stats Aren't Enough — the anomaly no z-score sees

   Catch the multivariate anomaly a single z-score misses — where each metric looks normal on its own but the combination has never occurred — using an ensemble of isolation forest and autoencoder.

4. 4

   Alert Without the Fatigue — one incident, one alert

   Turn a storm of anomaly points into a single, well-ranked, actionable alert — so the 14:32 incident pages once with context, not two hundred times, and the on-call team never learns to ignore the board.

5. 5

   See It, Own It — the dashboard the on-call lives in

   Make the whole pipeline visible and actionable: an engineer opens the dashboard mid-incident and answers in ten seconds — what spiked, when, how bad, still firing — then acknowledges, resolves, or silences the alert.

6. 6

   From Alert to Root Cause — the whole arc pays off

   Turn 'latency spiked 3× at 14:32' into 'the cause is a slow database query from deploy CHG-2207' — by correlating the anomaly with recent deploys, related metrics and past incidents — then track SLAs and error budgets so the system improves over time.

What you walk away with