Aporia Documentation
Get StartedBook a Demo🚀 Cool StuffBlog
V1
V1
  • Welcome to Aporia!
  • 🤗Introduction
    • Quickstart
    • Support
  • 💡Core Concepts
    • Why Monitor ML Models?
    • Understanding Data Drift
    • Analyzing Performance
    • Tracking Data Segments
    • Models & Versions
    • Explainability
  • 🏠Storing your Predictions
    • Overview
    • Real-time Models (Postgres)
    • Real-time Models (Kafka)
    • Batch Models
    • Kubeflow / KServe
    • Logging to Aporia directly
  • 🚀Model Types
    • Regression
    • Binary Classification
    • Multiclass Classification
    • Multi-Label Classification
    • Ranking
  • 📜NLP
    • Intro to NLP Monitoring
    • Example: Text Classification
    • Example: Token Classification
    • Example: Question Answering
  • 🍪Data Sources
    • Overview
    • Amazon S3
    • Athena
    • BigQuery
    • Delta Lake
    • Glue Data Catalog
    • PostgreSQL
    • Redshift
    • Snowflake
  • ⚡Monitors
    • Overview
    • Data Drift
    • Metric Change
    • Missing Values
    • Model Activity
    • Model Staleness
    • New Values
    • Performance Degradation
    • Prediction Drift
    • Value Range
    • Custom Metric
  • 📡Integrations
    • Slack
    • JIRA
    • New Relic
    • Single Sign On (SAML)
    • Webhook
    • Bodywork
  • 🔑API Reference
    • Custom Metric Definition Language
    • REST API
    • SDK Reference
    • Metrics Glossary
Powered by GitBook
On this page
  • Why Monitor Metric Change
  • Comparison methods
  • Customizing your monitor
  1. Monitors

Metric Change

PreviousData DriftNextMissing Values

Last updated 2 years ago

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.

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 metrics you would like to monitor

You may select as many prediction fields as you want (from raw inputs / features) 😊 the monitor will run on each selected field separately.

Our metric change monitor supports the following metrics:

  • Missing count

  • Average

  • Minimum

  • Maximum

  • Sum

  • Variance

  • STD

STEP 2: choose inspection period and baseline

For the fields you chose in the previous step, the monitor will raise an alert if the comparison between the inspection period and the baseline leads to a conclusion outside your threshold boundaries.

STEP 3: calibrate thresholds

This step is important to make sure you have the right amount of alerts that fits your needs. For anomaly detection method, use the monitor preview to help you decide what is the appropriate sensitivity level.

⚡
Change in percentage
Absolute value
Anomaly detection
Compared to segment
Compared to training