You create a weather forecasting model and need to build a pipeline that executes a processing script to load data from a datastore. The processed data must then be passed to a training script.

You implement the following solution:

from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep

# Define the processing step
processing_step = PythonScriptStep(
    name='process_data',
    script_name='processing_script.py',
    arguments=['--input_data', input_dataset],
    inputs=[input_dataset],
    compute_target=compute_cluster,
    source_directory=source_dir
)

# Define the training step
training_step = PythonScriptStep(
    name='train_model',
    script_name='training_script.py',
    arguments=['--input_data', processing_step.output],
    inputs=[processing_step.output],
    compute_target=compute_cluster,
    source_directory=source_dir
)

# Create and run the pipeline
pipeline = Pipeline(workspace=ws, steps=[processing_step, training_step])
pipeline_run = experiment.submit(pipeline)

from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep

# Define the processing step
processing_step = PythonScriptStep(
    name='process_data',
    script_name='processing_script.py',
    arguments=['--input_data', input_dataset],
    inputs=[input_dataset],
    compute_target=compute_cluster,
    source_directory=source_dir
)

# Define the training step
training_step = PythonScriptStep(
    name='train_model',
    script_name='training_script.py',
    arguments=['--input_data', processing_step.output],
    inputs=[processing_step.output],
    compute_target=compute_cluster,
    source_directory=source_dir
)

# Create and run the pipeline
pipeline = Pipeline(workspace=ws, steps=[processing_step, training_step])
pipeline_run = experiment.submit(pipeline)

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