Anomaly Detection in Practice
Find unusual events in data without drowning your team in false alarms
A taste of a lesson
Our isolation forest on server metrics fires 300 alerts a day. The on call team ignores them. What should I change?
Start with the budget, not the model. Ask the team how many alerts they can genuinely investigate, perhaps 5 to 10 a day, and set the threshold to the top scores that fit that budget. Then check what the current alerts look like: if many are normal daily peaks, add time of day and day of week context, or switch to residuals from a seasonal baseline. Record which alerts were useful so you can measure precision. What fraction of last week's alerts led to real action?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Define anomalies for a context with stakeholders, including contextual and collective types
- Apply robust statistical and seasonal baseline methods
- Use isolation forests, density scores and reconstruction error appropriately
- Evaluate detectors with scarce labels, injected anomalies and analyst feedback
- Set thresholds by alert budget and manage drift and alert fatigue
Lesson plan
- 1 What counts as an anomaly Define anomalies and their costs with stakeholders. Start
- 2 Robust statistics Flag outliers with methods that resist distortion. Start
- 3 Seasonal baselines Detect contextual anomalies in time series. Start
- 4 Machine learning detectors Apply isolation forests, density methods and autoencoders. Start
- 5 Evaluation without many labels Measure detector quality honestly. Start
- 6 Thresholds, drift and fatigue Run a detector sustainably in production. Start
Try asking
About this tutor
An advanced tutor for practitioners building systems that flag unusual transactions, sensor readings, metrics or behaviour. You will define what counts as an anomaly for your context, start with robust statistical methods and seasonal baselines, then move to isolation forests, density based scores and reconstruction error models. A large part of the course deals with what makes anomaly detection hard in practice: scarce labels, evaluating with injected anomalies and analyst feedback, alert budgets, thresholds, drift and alert fatigue. You finish with a design you could defend to an operations team.
Reviews
4.7
3 ratingsSample
- Stefan D.Sample
Setting thresholds by alert budget cut our noise drastically. The team trusts alerts again.
- Nadia O.Sample
Strong on evaluation without labels. Injected anomalies were useful, with good warnings about their limits.
- Ken T.Sample
The seasonal baseline lesson fixed our 'every Monday morning is an incident' problem.
About the teacher
Probability for machine learning, plus forecasting and anomaly detection
9 tutors 361 lessons taught Sample
I teach probability the way machine learning uses it: random variables, likelihood, entropy and simulation. I also teach two applied areas where probability matters every day: time series forecasting and anomaly detection. My work background is in monitoring and forecasting for operational systems, where wrong alarms and missed incidents both have a cost. I teach through small simulations, coin and...
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