Traces bugs or failures, identify patterns in your electronic records, label them in an efficient way or standardize your logs in different versions of the software.

  • Input: computer generated logs

  • Output: identified pattern

Anomaly Detection

Identifies unusual observations or behavior according to the usual patterns. This helps to detect faults, events in sensor networks and system disturbances as well as helps monitor the systems health.

  • Input: logs, usage data

  • Output: model detecting anomalies

Predictive Maintenance

Can save significant resources by scheduling maintenance of equipment based on estimated need.

  • Input: equipment usage data
  • Output: maintenance schedule

A/B Testing

Helps you make changes to your design or processes in a scientific manner by using data as a decision-base instead of hunches.

  • Input: explicit/ implicit feedback on the tested matter
  • Output: identification of the best version