Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.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?
Answer(s): B
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.Compare with this 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])
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.You must run the script as an Azure ML experiment on a compute cluster named aml-compute.You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml- compute that references the target compute cluster.Solution: Run the following code:Does the solution meet the goal?
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.Example:from azureml.train.sklearn import SKLearn}estimator = SKLearn(source_directory=project_folder,compute_target=compute_target,entry_script='train_iris.py')
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
Answer(s): A
DRAG DROP (Drag and Drop is not supported)You create machine learning models by using Azure Machine Learning.You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.You need to select the appropriate compute types.Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
Box 1: Compute cluster Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.Box 2: Inference cluster Box 3: Attached compute The compute types that can currently be attached for training include:A remote VM Azure Databricks (for use in machine learning pipelines)Azure Data Lake Analytics (for use in machine learning pipelines)Azure HDInsight Box 4: Compute cluster Note: There are four compute types:Compute instance Compute clusters Inference clusters Attached compute Note 2:Compute clusters Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.Attached compute To use compute targets created outside the Azure Machine Learning workspace, you must attach them. Attaching a compute target makes it available to your workspace. Use Attached compute to attach a compute target for training. Use Inference clusters to attach an AKS cluster for inferencing.Inference clusters Create or attach an Azure Kubernetes Service (AKS) cluster for large scale inferencing.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
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