Kedro Azure ML Plugin

Contents:

  • Introduction
    • What Azure ML Pipelines?
    • Why to integrate Kedro project with Azure ML Pipelines?
  • Installation
    • Prerequisites
    • Kedro setup
    • Plugin installation
      • Install from PyPI
      • Install from sources
    • Available commands
  • Quickstart
    • Video-tutorial
    • Prerequisites
    • Project initialization
    • Adjusting the Data Catalog
    • Pick your deployment option
      • (Option 1) Docker image flow
      • (Option 2) Code upload flow
    • Run the pipeline
    • Using a different compute cluster for specific nodes
    • Marking a node as deterministic
    • Distributed training
    • Run customization
  • MLflow Integration
  • Data Assets
    • Dataset Versioning
      • API Reference
    • Pipeline data passing
      • AzureMLPipelineDataset
        • AzureMLPipelineDataset.load()
        • AzureMLPipelineDataset.save()
    • V2 SDK
      • AzureMLAssetDataset
        • AzureMLAssetDataset.azure_config
        • AzureMLAssetDataset.load()
        • AzureMLAssetDataset.save()
    • V1 SDK
      • AzureMLPandasDataset
        • AzureMLPandasDataset.load()
        • AzureMLPandasDataset.save()
      • AzureMLFileDataset
        • AzureMLFileDataset.load()
        • AzureMLFileDataset.save()
  • Development
    • Prerequisites
    • Local development
    • Starting the job from local machine
Kedro Azure ML Plugin
  • Welcome to Kedro Azure ML Pipelines plugin documentation!
  • View page source

Welcome to Kedro Azure ML Pipelines plugin documentation!

Contents:

  • Introduction
    • What Azure ML Pipelines?
    • Why to integrate Kedro project with Azure ML Pipelines?
  • Installation
    • Prerequisites
    • Kedro setup
    • Plugin installation
    • Available commands
  • Quickstart
    • Video-tutorial
    • Prerequisites
    • Project initialization
    • Adjusting the Data Catalog
    • Pick your deployment option
    • Run the pipeline
    • Using a different compute cluster for specific nodes
    • Marking a node as deterministic
    • Distributed training
    • Run customization
  • MLflow Integration
  • Data Assets
    • Dataset Versioning
    • Pipeline data passing
    • V2 SDK
    • V1 SDK
  • Development
    • Prerequisites
    • Local development
    • Starting the job from local machine

Indices and tables

  • Index

  • Module Index

  • Search Page

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