Designer sample pipelines
Use the built-in examples in Azure Machine Learning designer to quickly get started building your own machine learning pipelines. The Azure Machine Learning designer GitHub repository contains detailed documentation to help you understand some common machine learning scenarios.
- An Azure subscription. If you don't have an Azure subscription, create a free account.
- An Azure Machine Learning workspace with the Enterprise SKU.
How to use sample pipelines
The designer saves a copy of the sample pipelines to your studio workspace. You can edit the pipeline to adapt it to your needs and save it as your own. Use them as a starting point to jumpstart your projects.
Open a sample pipeline
Sign in to ml.azure.com, and select the workspace you want to work with.
Select a sample pipeline under the New pipeline section.
Select Show more samples for a complete list of samples.
Submit a pipeline run
To run a pipeline, you first have to set default compute target to run the pipeline on.
In the Settings pane to the right of the canvas, select Select compute target.
In the dialog that appears, select an existing compute target or create a new one. Select Save.
Select Submit at the top of the canvas to submit a pipeline run.
Depending on the sample pipeline and compute settings, runs may take some time to complete. The default compute settings have a minimum node size of 0, which means that the designer must allocate resources after being idle. Repeated pipeline runs will take less time since the compute resources are already allocated. Additionally, the designer uses cached results for each module to further improve efficiency.
Review the results
After the pipeline finishes running, you can review the pipeline and view the output for each module to learn more.
Use the following steps to view module outputs:
Select a module in the canvas.
In the module details pane to the right of the canvas, select Outputs + logs. Select the graph icon to see the results of each module.
Use the samples as starting points for some of the most common machine learning scenarios.
Learn more about the built-in regression samples.
|Sample 1: Regression - Automobile Price Prediction (Basic)||Predict car prices using linear regression.|
|Sample 2: Regression - Automobile Price Prediction (Advanced)||Predict car prices using decision forest and boosted decision tree regressors. Compare models to find the best algorithm.|
Learn more about the built-in classification samples. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
|Sample 3: Binary Classification with Feature Selection - Income Prediction||Predict income as high or low, using a two-class boosted decision tree. Use Pearson correlation to select features.|
|Sample 4: Binary Classification with custom Python script - Credit Risk Prediction||Classify credit applications as high or low risk. Use the Execute Python Script module to weight your data.|
|Sample 5: Binary Classification - Customer Relationship Prediction||Predict customer churn using two-class boosted decision trees. Use SMOTE to sample biased data.|
|Sample 7: Text Classification - Wikipedia SP 500 Dataset||Classify company types from Wikipedia articles with multiclass logistic regression.|
|Sample 12: Multiclass Classification - Letter Recognition||Create an ensemble of binary classifiers to classify written letters.|
Learn more about the built-in recommender samples. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
|Sample 10: Recommendation - Movie Rating Tweets||Build a movie recommender engine from movie titles and rating.|
Learn more about the samples that demonstrate machine learning utilities and features. You can learn more about the samples without documentation links by opening the samples and viewing the module comments instead.
|Sample 6: Use custom R script - Flight Delay Prediction|
|Sample 8: Cross Validation for Binary Classification - Adult Income Prediction||Use cross validation to build a binary classifier for adult income.|
|Sample 9: Permutation Feature Importance||Use permutation feature importance to compute importance scores for the test dataset.|
|Sample 11: Tune Parameters for Binary Classification - Adult Income Prediction||Use Tune Model Hyperparameters to find optimal hyperparameters to build a binary classifier.|
Clean up resources
You can use the resources that you created as prerequisites for other Azure Machine Learning tutorials and how-to articles.
If you don't plan to use anything that you created, delete the entire resource group so you don't incur any charges.
In the Azure portal, select Resource groups on the left side of the window.
In the list, select the resource group that you created.
Select Delete resource group.
Deleting the resource group also deletes all resources that you created in the designer.
Delete individual assets
In the designer where you created your experiment, delete individual assets by selecting them and then selecting the Delete button.
The compute target that you created here automatically autoscales to zero nodes when it's not being used. This action is taken to minimize charges. If you want to delete the compute target, take these steps:
You can unregister datasets from your workspace by selecting each dataset and selecting Unregister.
To delete a dataset, go to the storage account by using the Azure portal or Azure Storage Explorer and manually delete those assets.
Learn how to build and deploy machine learning models with Tutorial: Predict automobile price with the designer