Aporia Documentation
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  • Welcome to Aporia!
  • 🤗Introduction
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    • Why Monitor ML Models?
    • Understanding Data Drift
    • Analyzing Performance
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  • 🏠Storing your Predictions
    • Overview
    • Real-time Models (Postgres)
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  • 🚀Model Types
    • Regression
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  • 📜NLP
    • Intro to NLP Monitoring
    • Example: Text Classification
    • Example: Token Classification
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    • Overview
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  • ⚡Monitors
    • Overview
    • Data Drift
    • Metric Change
    • Missing Values
    • Model Activity
    • Model Staleness
    • New Values
    • Performance Degradation
    • Prediction Drift
    • Value Range
    • Custom Metric
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  • 🔑API Reference
    • Custom Metric Definition Language
    • REST API
    • SDK Reference
    • Metrics Glossary
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On this page
  • Why Monitor New Values?
  • Comparison methods
  • Customizing your monitor
  1. Monitors

New Values

PreviousModel StalenessNextPerformance Degradation

Last updated 2 years ago

Why Monitor New Values?

Monitoring new values of categorical fields helps to locate and examine changes in the model's input.

For example, setting the monitor for a feature named state will help us discover a new region for which the model is asked to predict results.

Comparison methods

For this monitor, the following comparison methods are available:

Customizing your monitor

Configuration may slightly vary depending on the comparison method you choose.

STEP 1: choose the fields you would like to monitor

You may select as many fields as you want (from features/raw inputs) 😊

Note that the monitor will run on each selected field separately.

STEP 2: choose inspection period and baseline

For the fields you chose in the previous step, the monitor will raise an alert if the amount of new values in the inspection period compared to the baseline's values exceeds your threshold.

STEP 3: calibrate thresholds

This step is important to make sure you have the right amount of alerts that fits your needs. You can always readjust it later if needed.

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Change in percentage
Compared to segment
Compared to training