Sample Azure Machine Learning pipeline demonstrating how to register and process a file dataset using parameterized pipeline arguments.
This pipeline accepts one variable argument in the form of a PipelineParameter which is used to specify the location of a file present in an Azure Machine Learning datastore to be processed. This sample is one piece of a larger solution where a file of interest is automatically moved from Azure Blob Storage into an AML Datastore via Azure Data Factory which can also be used to trigger the pipeline execution.
Note: Recommend running this notebook using an Azure Machine Learning compute instance using the preconfigured Python 3.6 - AzureML environment.
To build and run the sample pipeline contained in SamplePipeline.ipynb the following resources are required:
- Azure Machine Learning Workspace