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Metric Change

Why monitor metric change?

Monitoring and measuring changes in features / raw inputs metrics allows for early detection of basic problems or changes in the model's input data.

For example - we can monitor and detect a deviation of more than 20% from the average of the feature 'age' from the average the monitor was trained with.

Detection Methods

For this monitor type, you can select the following detection methods:

Metric Change Detection Methods

  • Absolute Values - The metric value is lower or higher than a specific value.
  • Anomaly Detection - Detects anomalies in the value of the metric in the inspected data and its value in a time period before the data was collected.
  • Change In Percentage - Detects change in the ratio between the metric value of the inspected data and its value in a time period before the data was collected.
  • Compared To Segment - Detects change in the ratio between the metric value of the inspected data and to its value in a different data segment.
  • Compared To Training - Detects change in the ratio between the metric value of the inspected data and its value calculated from the reported training set.

Configuration

Start from choosing the features / raw inputs you'd like to monitor. You can select as many as you want :-)

Metric Change Configuration

Next, choose the metric you'd like to monitor from the following options:

  • Missing Count
  • Average
  • Minimum
  • Maximum
  • Sum
  • Variance
  • Standard Deviation

Note that the monitor configuration may vary between the detection method you choose.