Aporia Documentation
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  • 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
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  • Why Monitor Model Staleness?
  • Configuring your monitor
  1. Monitors

Model Staleness

Why Monitor Model Staleness?

Monitoring the last time a model version was deployed helps track models that do not meet the organization's policy, or require high attention to track metrics and changes.

Configuring your monitor

The monitor will raise an alert when the model version is older than the specified time period.

You can choose time granularity to be hour, day, week or month.

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Last updated 2 years ago

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