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
    • 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
  • 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 StoreΒΆ

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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