Exasol MLflow Plugin Exasol MLflow Plugin Exasol MLflow Plugin
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  • main
  • 1.2.0
  • 1.1.0
  • 1.0.0
  • Features
    • BucketFS Artifact Store
    • Accessing the REST API with UDFs
  • User Guide
    • Installation
      • Installing the Plugin
      • Using a Script Language Container
      • UDFs and Adapter Script
      • Connection and Virtual Schema
    • Accessing the MLflow Server
      • REST API UDFs
      • Backend Store Via Virtual Schema
    • Accessing Models in UDFs
      • Alternatives for Loading an MLflow Model
      • MLflow Tracking URI
      • Using the BucketFS Artifact Store
        • As Default Artifact Store
        • In the Scope of an Individual MLflow Experiment
        • Storing MLflow Experiment Artifacts in BucketFS
        • URI Format of Artifact URIs
        • When and for Which Operations is the MLflow Plugin Required?
      • Creating and Running UDFs
    • Accessing the MLflow AI Gateway
  • Developer Guide
    • How the MLflow Plugin works
    • MLflow Server Processes
    • Building the SLC Image
    • Integration Tests
    • Generated Files
  • API Reference
  • FAQ
  • Changes
    • Unreleased
    • 2.0.0 - 2026-06-11
    • 1.1.0 - 2026-03-13
    • 1.0.0 - 2026-03-06
  1. Exasol MLflow Plugin /
  2. User Guide /
  3. Accessing Models in UDFs /
  4. Using the BucketFS Artifact Store

Using the BucketFS Artifact Store#

See the MLflow documentation for specifying the BucketFS Artifact Store. You can define this location either when starting the MLflow server or when creating an MLflow experiment.

../../../_images/enabling-bucketfs-artifact-store.svg
  • As Default Artifact Store
  • In the Scope of an Individual MLflow Experiment
  • Storing MLflow Experiment Artifacts in BucketFS
  • URI Format of Artifact URIs
  • When and for Which Operations is the MLflow Plugin Required?
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Alternatives for Loading an MLflow Model
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As Default Artifact Store

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