Aporia Documentation
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  • 📖Aporia Docs
  • 🤗Introduction
    • Quickstart
    • Support
  • 💡Core Concepts
    • Why Monitor ML Models?
    • Understanding Data Drift
    • Analyzing Performance
    • Tracking Data Segments
    • Models & Versions
  • 🚀Deployment
    • AWS
    • Google Cloud
    • Azure
    • Databricks
    • Offline / On-Prem
    • Platform Architecture
  • 🏠Storing your Predictions
    • Overview
    • Real-time Models (Postgres)
    • Real-time Models (Kafka)
    • Batch Models
    • Kubeflow / KServe
  • 🧠Model Types
    • Regression
    • Binary Classification
    • Multiclass Classification
    • Multi-Label Classification
    • Ranking
  • 🌈Explainability
    • SHAP values
  • 📜NLP
    • Intro to NLP Monitoring
    • Example: Text Classification
    • Example: Token Classification
    • Example: Question Answering
  • 🍪Data Sources
    • Overview
    • Amazon S3
    • Athena
    • BigQuery
    • Databricks
    • Glue Data Catalog
    • Google Cloud Storage
    • PostgreSQL
    • Redshift
    • Snowflake
    • Microsoft SQL Server
    • Oracle
  • ⚡Monitors & Alerts
    • Overview
    • Data Drift
    • Metric Change
    • Missing Values
    • Model Activity
    • Model Staleness
    • Performance Degradation
    • Prediction Drift
    • Value Range
    • Custom Metric
    • New Values
    • Alerts Consolidation
  • 🎨Dashboards
    • Overview
  • 🤖ML Monitoring as Code
    • Getting started
    • Adding new models
    • Data Segments
    • Custom metrics
    • Querying metrics
    • Monitors
    • Dashboards
  • 📡Integrations
    • Slack
    • Webhook
    • Teams
    • Single Sign On (SAML)
    • Cisco
  • 🔐Administration
    • Role Based Access Control (RBAC)
  • 🔑API Reference
    • REST API
    • API Extended Reference
    • Custom Segment Syntax
    • Custom Metric Syntax
    • Code-Based Metrics
    • Metrics Glossary
  • ⏩Release Notes
    • Release Notes 2024
    • Release Notes 2023
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On this page
  • Why Monitor Model Staleness?
  • Configuring your monitor
  1. Monitors & Alerts

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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