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 Value Range?
  • Comparison methods
  • Customizing your monitor
  1. Monitors

Value Range

PreviousPrediction DriftNextCustom Metric

Last updated 2 years ago

Why Monitor Value Range?

Monitoring changes in the value range of numeric fields helps to locate and examine anomalies in the model's input.

For example, setting the monitor for a feature named hour_sin with the range -1 <= x <= 1 will help us discover issues in model input.

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 value range in the inspection period exceeds your threshold boundaries compared to the baseline's value range.

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.

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