You create a weather forecasting model using historical data and need to build a pipeline. This pipeline must execute a processing script to load data from a datastore, then pass the processed data to a training script for a machine learning model.

You implement the following solution:

# Code to create and run a pipeline
from azureml.core import Workspace, Dataset, Datastore
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep

# Define the workspace, compute target, and datastore
ws = Workspace.from_config()
compute_target = ws.compute_targets['cpu-cluster']
datastore = ws.get_default_datastore()

# Create a PipelineData object to pass data between steps
processed_data = PipelineData('processed_data', datastore=datastore)

# Step 1: Data processing step
process_step = PythonScriptStep(
    name='process-data',
    script_name='process.py',
    arguments=['--output_path', processed_data],
    outputs=[processed_data],
    compute_target=compute_target,
    source_directory='.'
)

# Step 2: Model training step
train_step = PythonScriptStep(
    name='train-model',
    script_name='train.py',
    arguments=['--input_data', processed_data],
    inputs=[processed_data],
    compute_target=compute_target,
    source_directory='.'
)

# Create and run the pipeline
pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
pipeline_run = pipeline.submit('pipeline-experiment')

Does this solution meet the goal?