Does the solution meet the goal?

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You create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?
A . Yes
B . No

View Answer

Answer: A

Explanation:

The two steps are present: process_step and train_step Data_input correctly references the data in the data store.

Note:

Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azureml.pipeline.core import Pipeline, PipelineData

from azureml.pipeline.steps import PythonScriptStep

datastore = ws.get_default_datastore()

process_step_output = PipelineData("processed_data", datastore=datastore)

process_step = PythonScriptStep(script_name="process.py",

arguments=["–data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory)

train_step = PythonScriptStep(script_name="train.py",

arguments=["–data_for_train", process_step_output],

inputs=[process_step_output],

compute_target=aml_compute,

source_directory=train_directory)

pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

Reference: https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?

view=azure-ml-py