Aporia Documentation
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  • 📖Aporia Docs
  • 🤗Introduction
    • Quickstart
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  • 💡Core Concepts
    • Why Monitor ML Models?
    • Understanding Data Drift
    • Analyzing Performance
    • Tracking Data Segments
    • Models & Versions
  • 🚀Deployment
    • AWS
    • Google Cloud
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    • Databricks
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    • Platform Architecture
  • 🏠Storing your Predictions
    • Overview
    • Real-time Models (Postgres)
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    • Kubeflow / KServe
  • 🧠Model Types
    • Regression
    • Binary Classification
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  • 🌈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
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  • ⚡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
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  • 🔐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
  • Configure Data Source
  • Link Your Data
  • You are good to go, it's time to get value!
  1. Data Sources

Overview

PreviousExample: Question AnsweringNextAmazon S3

Last updated 1 year ago

Aporia monitors your models by connecting directly to your data. If you don't store your predictions yet, see our guide on .

Aporia currently supports the following data sources:

  • Amazon S3

  • Athena

  • BigQuery

  • Databricks

  • Glue Data Catalog

  • Google Cloud Storage

  • PostgreSQL

  • Redshift

  • Snowflake

  • Azure Blob Storage

  • MSSQL

If your storage or database are not shown here, please contact your Aporia account manager for further assistance.

Configure Data Source

Connecting to a data source begins with configuring its connection details. For example, to connect to a Postgres database, we can create a data source using the following details:

url="jdbc:postgresql://<POSTGRES_HOSTNAME>/<DBNAME>",
user="<DB_USER>",
password="<DB_PASSWORD>"

Please refer to the documentation page of the relevant data source for a complete list of requirements and configuration options.

Link Your Data

After creating a data source, we can create a model version and link our data. This process consist of two steps:

  1. Linking a dataset - define a query to retrieve your data.

  2. Mapping model schema - map your model's raw inputs, features, predictions, and actuals. The fields you can map are from the columns you retrieved.

The ground truth can be NULL until it actually has value, that's okay.

You are good to go, it's time to get value!

🍪
🎉
Storing Your Predictions