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:

from azureml.core import Workspace, Dataset, Experiment
from azureml.core.runconfig import RunConfiguration
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep

# Define the data store and dataset
datastore = ws.get_default_datastore()
dataset = Dataset.get_by_name(ws, 'weather-data')

# Define the output for the processing step
processed_data = PipelineData('processed_data', datastore=datastore)

# Configure the processing step
processing_step = PythonScriptStep(
    name='process-data',
    script_name='process.py',
    arguments=['--input', dataset.as_named_input('input').as_mount(), '--output', processed_data],
    outputs=[processed_data],
    compute_target=compute_target,
    runconfig=run_config
)

# Configure the training step
training_step = PythonScriptStep(
    name='train-model',
    script_name='train.py',
    arguments=['--input', processed_data],
    inputs=[processed_data],
    compute_target=compute_target,
    runconfig=run_config
)

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

Does the solution meet the goal?