Kedro Azure ML Plugin
stable
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
API Reference
Pipeline data passing
AzureMLPipelineDataset
V2 SDK
AzureMLAssetDataset
V1 SDK
AzureMLPandasDataset
AzureMLFileDataset
Development
Prerequisites
Local development
Starting the job from local machine
Kedro Azure ML Plugin
Index
Edit on GitHub
Index
A
A
azure_config (kedro_azureml.datasets.asset_dataset.AzureMLAssetDataset property)
AzureMLAssetDataset (class in kedro_azureml.datasets.asset_dataset)
AzureMLFileDataset (class in kedro_azureml.datasets)
AzureMLPandasDataset (class in kedro_azureml.datasets)
AzureMLPipelineDataset (class in kedro_azureml.datasets)
Read the Docs
v: stable
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