Complete the Lab
Lab: Detect Times Series Anomalies
Use Case: Predictive Maintenance
Quickly identifying anomalies can save your organization from unscheduled downtime, as well as money and its reputation. On this mission, you will build an anomaly detection model and use a labeled test set to improve the model.
Mission format and duration: self-paced, hands-on, 1 hour
Upon completion of this mission, you will be able to:
- Build a time-aware unsupervised ML model to detect anomalies in the predictive maintenance dataset
- Evaluate anomaly models using the Synthetic Area Under the Curve (AUC) metric
- Analyze and compare anomaly detection models using the Anomaly Over Time tool
- Compare models by uploading a labeled test set and using the Confusion Matrix and Anomaly Assessment tools
Who should complete this mission?
- Business Analysts
- Citizen Data Scientists
- Data Scientists
Before embarking on this mission, you should complete one of the following:
- Starter Quest appropriate for your role (self-paced)
- AutoML I (virtual instructor-led mission)
- DataRobot for Data Scientists (virtual instructor-led mission)
We also recommend that you also complete the following before beginning this mission:
- Time Series Starter (self-paced mission)
- Chrome browser
- DataRobot Automated Machine Learning — If you don’t have access to the application, please sign up for our free trial: datarobot.com/trial.