├── .gitignore ├── README.md └── aml-visual-interface ├── environment-notes.md ├── images ├── 01.png ├── 02.png └── 03.png ├── lab-01 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ └── 35.png ├── lab-02 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 11_1.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ └── 21.png ├── lab-03 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 091.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ └── 13.png ├── lab-04 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 11_1.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── E01.png │ ├── E02.png │ ├── E03.png │ ├── E05.png │ ├── E06.png │ ├── E07.png │ └── E08.png ├── lab-05 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 13_1.png │ ├── 13_2.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 23.png │ ├── 24_1.png │ └── 24_2.png ├── lab-06 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 13_1.png │ ├── 13_2.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 23.png │ ├── 24_1.png │ └── 24_2.png ├── lab-07 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 04.png │ ├── 05.png │ ├── 05_1.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 09_1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 18_1.png │ ├── 18_2.png │ └── 19.png ├── lab-08 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ └── 13.png ├── lab-09 ├── README.md └── images │ ├── 00.png │ ├── 01_1.png │ ├── 01_2.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ └── 10.png ├── lab-10 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ └── 19.png ├── lab-11 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── udacity-01a.png │ ├── udacity-02.png │ └── udacity-03.png ├── lab-12 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ └── 19.png ├── lab-13 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13_1.png │ ├── 13_2.png │ ├── 13_3.png │ ├── 13_4.png │ ├── 13_5.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 19.png │ ├── 20.png │ ├── 23.png │ └── 24.png ├── lab-14 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ └── 23.png ├── lab-15 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 06a.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 16_1.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ └── 24.png ├── lab-16 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 20.png │ ├── 21.png │ ├── 21_0.png │ ├── 22.png │ ├── 23.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ └── 28.png ├── lab-17 ├── README.md └── images │ ├── 00.png │ ├── 01.png │ ├── 01a.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ └── 17.png ├── lab-18 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── application-uri.png │ ├── compute-instance-details.png │ ├── compute-instances.png │ ├── new_compute_1.png │ ├── new_compute_2.png │ └── selected-compute-instance.png ├── lab-19 ├── README.md ├── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── diabetes-experiment.png │ ├── experiments.png │ ├── model-files.png │ ├── model-files1.png │ ├── new_compute_1.png │ ├── new_compute_2.png │ ├── notebook-link.png │ └── upload.png └── notebook │ └── 1st-experiment-sdk-train-model.ipynb ├── lab-20 ├── README.md ├── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── added-columns.png │ ├── diabetes-experiment.png │ ├── edit-table.png │ ├── experiments.png │ ├── model-files.png │ ├── new_compute_1.png │ ├── new_compute_2.png │ ├── notebook-link.png │ ├── run-details.png │ ├── run-links.png │ ├── run-outputs.png │ └── upload.png └── notebook │ └── 1st-experiment-sdk-train-model.ipynb ├── lab-21 ├── README.md └── images │ ├── 00.png │ ├── 01a.png │ ├── consume.png │ ├── deploy-succeeded.png │ ├── deploy-web-service.png │ ├── endpoints.png │ ├── new-pipeline.png │ ├── new-real-time-inference-pipeline.png │ ├── run-button.png │ ├── run-inference-pipeline.png │ ├── select-compute-target-link.png │ ├── set-up-compute-target.png │ ├── set-up-inference-pipeline-run.png │ ├── set-up-pipeline-run.png │ └── set-up-real-time-endpoint.png ├── lab-22 ├── README.md ├── images │ ├── 00.png │ ├── 01a.png │ ├── 02.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── aml-values.png │ ├── new_compute_1.png │ ├── new_compute_2.png │ ├── notebook-link.png │ └── upload.png └── notebook │ ├── automl_dependencies.yml │ └── deployment-with-AML.ipynb └── lab-23 ├── README.md ├── images ├── 00.png ├── 01a.png ├── 02.png ├── 03.png ├── 05.png ├── 06.png ├── 07.png ├── new_compute_1.png ├── new_compute_2.png └── upload.png └── notebook └── interpretability-with-AML.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | ## Ignore Visual Studio temporary files, build results, and 2 | ## files generated by popular Visual Studio 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It supports Azure Machine Learning compute, GPU or CPU. Azure Machine Learning designer also supports publishing models as web services on Azure Kubernetes Service that can easily be consumed by other applications. To use Azure Machine Learning designer, you do not need programming experience and this quickstart will walk you through an exercise that will show how to process training data, create a model, train, score, and evaluate the model and finally deploy the trained model as a web service. 4 | 5 | ## Lesson 1 6 | 7 | *Concept 9 - The Models* 8 | 9 | Lab 1: [Use an algorithm (linear regression) to train a model](./aml-visual-interface/lab-01/README.md) 10 | 11 | ## Lesson 2 12 | 13 | *Concept 1 - Data Import and Transformation* 14 | 15 | Lab 2: [Import, transform, and export data](./aml-visual-interface/lab-02/README.md) 16 | 17 | *Concept 2 - Manage Data* 18 | 19 | Lab 3: [Create and version a dataset](./aml-visual-interface/lab-03/README.md) 20 | 21 | 22 | Lab 4: [Engineer and select features](./aml-visual-interface/lab-04/README.md) 23 | 24 | *Concept 3 - Model Training Basics* 25 | 26 | Lab 5: [Train and evaluate a model](./aml-visual-interface/lab-05/README.md) 27 | 28 | *Concept 4 - Ensembles* 29 | 30 | Lab 6: [Train a two-class decision forest](./aml-visual-interface/lab-06/README.md) 31 | 32 | Lab 7: [Train a simple classifier with Automated ML](./aml-visual-interface/lab-07/README.md) 33 | 34 | ## Lesson 3 35 | 36 | *Concept 1 - Supervised Learning, Classification* 37 | 38 | Lab 8: [Compare the performance of the various two-class classifiers](./aml-visual-interface/lab-08/README.md) 39 | 40 | Lab 9: [Compare the performance of the various multiclass classifiers](./aml-visual-interface/lab-09/README.md) 41 | 42 | *Concept 2 - Classifier using Automated Machine Learning* 43 | 44 | Lab 10: [Train a classifier using automated machine learning](./aml-visual-interface/lab-10/README.md) 45 | 46 | *Concept 3 - Supervised Learning, Regression* 47 | 48 | Lab 11: [Compare the performance of the various regressors](./aml-visual-interface/lab-11/README.md) 49 | 50 | *Concept 4 - Regression using Automated Machine Learning* 51 | 52 | Lab 12: [Train a regressor using automated machine learning](./aml-visual-interface/lab-12/README.md) 53 | 54 | *Concept 6 - CLustering* 55 | 56 | Lab 13: [Train a simple clustering model](./aml-visual-interface/lab-13/README.md) 57 | 58 | ## Lesson 4 59 | 60 | *Concept 1 - A taste of deep learning* 61 | 62 | Lab 14: [Classical ML vs. Deep Learning: multiclass neural net module](./aml-visual-interface/lab-14/README.md) 63 | 64 | *Concept 3 - Similarity learning recommendation* 65 | 66 | Lab 15: [Train a simple recommender](./aml-visual-interface/lab-15/README.md) 67 | 68 | *Concept 4 - Text classification* 69 | 70 | Lab 16: [Train a simple text classifier](./aml-visual-interface/lab-16/README.md) 71 | 72 | *Concept 7 - Forecasting* 73 | 74 | Lab 17: [Train a time-series forecasting model using automated machine learning](./aml-visual-interface/lab-17/README.md) 75 | 76 | 77 | ## Lesson 5 78 | 79 | *Concept 2 - Compute Resources* 80 | 81 | Lab 18: [Managing compute](./aml-visual-interface/lab-18/README.md) 82 | 83 | Lab 19: [Train a machine learning model from a managed notebook environment](./aml-visual-interface/lab-19/README.md) 84 | 85 | *Concept 3 - Basic Modeling* 86 | 87 | Lab 20: [Explore experiments and runs](./aml-visual-interface/lab-20/README.md) 88 | 89 | *Concept 5 - Operationalizing Models* 90 | 91 | Lab 21: [Deploy a trained model as a webservice](./aml-visual-interface/lab-21/README.md) 92 | 93 | *Concept 6 - Programatically Accessing Managed Services* 94 | 95 | Lab 22: [Training and deploying a model from a notebook running in a Compute Instance](./aml-visual-interface/lab-22/README.md) 96 | 97 | ## Lesson 6 98 | 99 | *Concept 2 - Model transparency and explainability* 100 | 101 | Lab 23: [Explore model explanations](./aml-visual-interface/lab-23/README.md) 102 | -------------------------------------------------------------------------------- /aml-visual-interface/environment-notes.md: -------------------------------------------------------------------------------- 1 | # Exercise 1: Setting up your environment 2 | 3 | If a lab environment has not be provided for you, this lab provides the instructions to get started in your own Azure Subscription. 4 | 5 | The labs have the following requirements: 6 | - Azure subscription. You will need a valid and active Azure account to complete this Azure lab. If you do not have one, you can sign up for a [free trial](https://azure.microsoft.com/en-us/free/). 7 | 8 | ## Azure Quotas Required 9 | The quickstart depend on the capability to utilize a certain quantity of Azure resources, for which your Azure subscription will need to have sufficient quota available. 10 | 11 | The following are the specific quotas required, if your subscription does not meet the quota requirements in the region in which you will perform the quickstart, you will need to request a quota increase thru Azure support: 12 | 13 | Compute-VM 14 | - Quota: Standard Dv2 Family vCPUs 15 | - Provider: Microsoft.Compute 16 | - SKU family: Dv2 Series 17 | - Required Limit: 14 18 | 19 | Compute-VM 20 | - Quota: Total Regional vCPUs 21 | - Provider: Microsoft.Compute 22 | - SKU family: Dv2 Series 23 | - Required Limit: 14 24 | 25 | ## Prerequisites 26 | 27 | - Create an Azure resource group named: `QuickStarts`. See [Create Resource Groups](https://docs.microsoft.com/en-us/azure/azure-resource-manager/manage-resource-groups-portal) for details on how to create the resource group. 28 | 29 | - Create an Azure Machine Learning service workspace, **enterprise edition**, named: `quick-starts-ws`. See [Create an Azure Machine Learning Service Workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) for details on how to create the workspace. 30 | 31 | Next, we will upfront create two Azure Machine Learning Computes, one for running machine learning experiments and the other for deploying trained models. Creating computes can take up to 5 minutes, thus we can start the creation and move on to the quickstart to conserve time. 32 | 33 | ## Task 1: Create Azure Machine Learning Compute 34 | 35 | Create a compute target in the workspace `quick-starts-ws` to run your Azure Machine Learning experiments. 36 | 37 | 1. In Azure Portal, open the machine learning workspace: `quick-starts-ws` 38 | 39 | 2. Select **Launch the new Azure Machine Learning studio** 40 | 41 | ![Select Launch the new Azure Machine Learning studio.](images/01.png 'Launch the new Azure Machine Learning studio') 42 | 43 | 3. From the studio, select **Compute, Training Clusters, + New**. This will open the `New Training Cluster` dialog on the right. In the dialog, enter the following information and then select **Create**: 44 | 45 | a. Compute name: `qs-compute` 46 | 47 | b. Virtual machine size: `Standard_D2_v2` 48 | 49 | c. Minimum number of nodes: `1` 50 | 51 | d. Maximum number of nodes: `1` 52 | 53 | ![Image highlights the steps to open the create new training cluster dialog, and shows the information to provide for the different fields in the dialog.](images/02.png 'New Training Cluster') 54 | 55 | ## Task 2: Create Kubernetes Service Compute 56 | 57 | Next, we will create a Kubernetes Service Compute to publish the trained model as web service. 58 | 59 | 1. From the studio, select **Compute, Inference Clusters, + New**. This will open the `New Inference Cluster` dialog on the right. In the dialog, enter the following information and then select **Create**: 60 | 61 | a. Compute name: `nyc-taxi-service` 62 | 63 | b. Region: `East US` 64 | 65 | c. Virtual machine size: `Standard_D3_v2` 66 | 67 | d. 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Create an Azure Machine Learning datasets to interact with data in your datastores and package your data into a consumable object for machine learning tasks. Register the dataset to your workspace to share and reuse it across different experiments without data ingestion complexities. 6 | 7 | Datasets can be created from local files, public urls, Azure Open Datasets, or specific file(s) in your datastores. To create a dataset from an in memory pandas dataframe, write the data to a local file, like a csv, and create your dataset from that file. Datasets aren't copies of your data, but are references that point to the data in your storage service, so no extra storage cost is incurred. 8 | 9 | In this lab, we are using a subset of NYC Taxi & Limousine Commission - green taxi trip records available from [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/) to show how you can register and version a Dataset using the AML designer interface. In the first exercises we use a modified version of the original CSV file, which includes collected records for five months (January till May). The second exercise demonstrates how we can create a new version of the initial dataset when new data is collected (in this case, we included records collected in June in the CSV file). 10 | 11 | ## Exercise 1: Register Dataset with Azure Machine Learning studio 12 | 13 | ### Task 1: Upload Dataset from web file 14 | 15 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 16 | 17 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 18 | 19 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 20 | 21 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 22 | 23 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 24 | 25 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 26 | 27 | 4. From the studio, select **Datasets, + Create dataset, From web files**. This will open the `Create dataset from web files` dialog on the right. 28 | 29 | ![Image highlights the steps to open the create dataset from web files dialog.](images/02.png 'Create dataset from web files') 30 | 31 | 5. Provide the following information and then select **Next**: 32 | 33 | 1. Web URL: `https://introtomlsampledata.blob.core.windows.net/data/nyc-taxi/nyc-taxi-sample-data-5months.csv` 34 | 35 | 1. Name: `nyc-taxi-sample-dataset` 36 | 37 | ![Enter the name and URL for the dataset.](images/03.png 'Basic info for dataset') 38 | 39 | ### Task 2: Preview Dataset 40 | 41 | 1. On the Settings and preview panel, set the **Column headers** drop down to `All files have same headers`. 42 | 43 | 2. Scroll the data preview to right to observe the target column: `totalAmount`. After you are done reviewing the data, select **Next** 44 | 45 | ![Scroll right to review dataset.](images/05.png 'Review dataset') 46 | 47 | ### Task 3: Select Columns 48 | 49 | 1. Select columns from the dataset to include as part of your training data. Leave the default selections and select **Next** 50 | 51 | ![Select columns from the dataset to include as part of your training data.](images/06.png 'Select columns') 52 | 53 | ### Task 4: Create Dataset 54 | 55 | 1. Confirm the dataset details and select **Create** 56 | 57 | ![Confirm the details of the dataset you uploaded and then select Create.](images/07.png 'Confirm and create the dataset') 58 | 59 | ## Exercise 2: Create a version of the existing Dataset 60 | 61 | ### Task 1: Register new dataset version 62 | 63 | 1. From the [Azure Machine Learning studio](https://ml.azure.com/), select **Datasets** and select the `nyc-taxi-sample-dataset` dataset created in the first exercise. This will open the `Dataset details` page. 64 | 65 | 2. Select **New version, From web files** to open the same `Create dataset from web files` dialog you already entered in the first exercise. 66 | 67 | ![Dataset details page with New version option.](images/08.png 'New version from web files in dataset details page') 68 | 69 | 3. This time, the **Name** and **Dataset version** fields are already filled in for you. Provide the following information and select **Next** to move on to the next step: 70 | 71 | 1. Web URL: `https://introtomlsampledata.blob.core.windows.net/data/nyc-taxi/nyc-taxi-sample-data-6months.csv` 72 | 73 | ![Basic info to create dataset version from local files.](images/09.png 'Basic info to create dataset version from local files') 74 | 75 | 4. Select `All files have the same headers` in the **Column headers** drop-down and move on to the schema selection step. 76 | 77 | 5. On the `Schema` page, let's suppose you decided to exclude some columns from your dataset. Exclude columns: **snowDepth**, **prcipTime**, **precipDepth**. Select **Next** to move on to the final step. 78 | 79 | ![Schema selection page for version 2 of the dataset.](images/091.png 'Schema selection page for version 2 of the dataset') 80 | 81 | 6. Notice the `Dataset version` value in the basic info section. Select **Create** to close the new version confirmation page. 82 | 83 | ![Confirmation page for new dataset version.](images/10.png 'Confirmation page for new dataset version') 84 | 85 | ### Task 2: Review both versions of the dataset 86 | 87 | 1. Back to the **Datasets** page, in the **Registered datasets** list, notice the version value for the `nyc-taxi-sample-dataset` dataset. 88 | 89 | ![Review the dataset current version.](images/11.png 'Review the dataset current version.') 90 | 91 | 2. Select the `nyc-taxi-sample-dataset` dataset link to open the dataset details page, where **Version 2(latest)** is automatically selected. Go to the **Explore** section to observe the structure and content of the new version. Notice the columns and rows structure in the dataset preview pane: 92 | 93 | - **Number of columns**: 11 94 | - **Number of rows**: 10000 95 | - Scroll right to check that the three excluded columns are missing (**snowDepth**, **prcipTime**, **precipDepth**) 96 | 97 | ![Review the structure of the current version.](images/12.png 'Review the structure of current version.') 98 | 99 | 3. Select **Version 1** from the drop-down near the dataset name title and notice the changing values for: 100 | 101 | - **Number of columns**: 14 (since the previous version still contains the three excluded columns) 102 | - **Number of rows**: 9776 (since the previous version contains only data for 5 months) 103 | 104 | ![Review the structure of the previous version.](images/13.png 'Review the structure of the previous version.') 105 | 106 | ## Next Steps 107 | 108 | Congratulations! 109 | You have now explored a first simple scenario for dataset versioning using the Azure Machine Learning studio. 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It supports Azure Machine Learning compute, GPU or CPU. Machine Learning designer also supports publishing models as web services on Azure Kubernetes Service that can easily be consumed by other applications. 4 | 5 | In this lab, we will be compare the performance of two binary classifiers: Two-Class Boosted Decision Tree and Two-Class Logistic Regression for predicting customer churn. The goal is to run an expensive marketing campaign for high risk customers; thus, the **precision** metric is going to be key in evaluating performance of these two algorithms. We will do all of this from the Azure Machine Learning designer without writing a single line of code. 6 | 7 | # Exercise 1: Create Training Pipeline 8 | 9 | ## Task 1: Open Sample 5: Binary Classification – Customer Relationship Prediction 10 | 11 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 12 | 13 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 14 | 15 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 16 | 17 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 18 | 19 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 20 | 21 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 22 | 23 | 4. From the studio, select **Designer, Sample 5: Binary Classification – Customer Relationship Prediction**. 24 | 25 | ![Image highlights the steps to open the sample pipeline, Sample 5: Binary Classification – Customer Relationship Prediction.](images/01.png 'Pipeline Authoring Editor') 26 | 27 | ## Task 2: Setup Compute Target 28 | 29 | 1. In the settings panel on the right, select **Select compute target**. 30 | 31 | ![Image highlights the link to select to open the setup compute target editor.](images/02.png 'Setup Compute Target') 32 | 33 | 2. In the `Set up compute target` editor, select the available compute, and then select **Save**. 34 | 35 | >> Note: If you are facing difficulties in accessing pop-up windows or buttons in the user interface, please refer to the Help section in the lab environment. 36 | 37 | ![Image shows how to select the existing compute target named qs-compute.](images/03.png 'Setup Compute Target') 38 | 39 | ## Task 3: Delete Pipeline Modules 40 | 41 | 1. From the `right-hand-side` of the pipeline, select the **Two-Class Boosted Decision Tree module** and then select the **Delete Icon**. 42 | 43 | ![Image shows how to delete an existing module.](images/04.png 'Delete Module') 44 | 45 | 2. From the `right-hand-side` of the pipeline, select the **SMOTE module** and then select the **Delete Icon**. 46 | 47 | ![Image shows how to delete an existing module.](images/05.png 'Delete Module') 48 | 49 | ## Task 4: Setup the Two-Class Logistic Regression Module 50 | 51 | 1. Select **Machine Learning Algorithms** section in the left navigation. Follow the steps outlined below: 52 | 53 | 1. Select the **Two-Class Logistic Regression** prebuilt module 54 | 55 | 2. Drag and drop the selected module on to the canvas 56 | 57 | 3. Connect the `Two-Class Logistic Regression` module to the first input of the `Train Model` module 58 | 59 | 4. Connect the first output of the `Split Data` module to the second input of the `Train Model` module 60 | 61 | ![Image shows the steps to add and configure the Two-Class Logistic Regression module.](images/13.png 'Two-Class Logistic Regression Module') 62 | 63 | # Exercise 2: Submit Training Pipeline 64 | 65 | ## Task 1: Create Experiment and Submit Pipeline 66 | 67 | 1. Select **Submit** to open the `Setup pipeline run` editor. 68 | 69 | ![Image shows where to select the submit button to open the setup pipeline run editor.](images/07.png 'Submit Pipeline') 70 | 71 | > Please note that the button name in the UI is changed from **Run** to **Submit**. 72 | 73 | 2. In the `Setup pipeline run editor`, select **Experiment, Create new** and provide `New experiment name:` **Churn-Predictor**, and then select **Submit**. 74 | 75 | ![Image shows how to provide the experiment name in the setup pipeline run editor and start the pipeline run.](images/08.png 'Submit Pipeline') 76 | 77 | 3. Wait for pipeline run to complete. It will take around **5 minutes** to complete the run. 78 | 79 | 4. While you wait for the model training to complete, you can learn more about the evaluation metrics for the classification algorithm used in this lab by selecting [Metrics for classification models](https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/evaluate-model#bkmk_classification). 80 | 81 | # Exercise 3: Compare Model Performance 82 | 83 | ## Task 1: Open Evaluation Results for Two-Class Boosted Decision Tree 84 | 85 | 1. From the `left-hand-side` of the pipeline, select **Evaluate Model, Outputs, Visualize** to open the `Evaluate Model result visualization` dialog for the `Two-Class Boosted Decision Tree` module. 86 | 87 | ![Image shows how to open the evaluate model result visualization dialog.](images/09.png 'Evaluate Model Results') 88 | 89 | ## Task 2: Evaluate Two-Class Boosted Decision Tree Performance 90 | 91 | 1. Scroll down to review model performance metrics for `Two-Class Boosted Decision Tree`. Observe that the **Precision** value is around **0.7**. 92 | 93 | ![Image shows the evaluate model result visualization dialog for Two-Class Boosted Decision Tree.](images/10.png 'Two-Class Boosted Decision Tree Performance') 94 | 95 | ## Task 3: Open Evaluation Results for Two-Class Logistic Regression 96 | 97 | 1. From the `right-hand-side` of the pipeline, select **Evaluate Model, Outputs, Visualize** to open the `Evaluate Model result visualization` dialog for the `Two-Class Logistic Regression` module. 98 | 99 | ![Image shows how to open the evaluate model result visualization dialog.](images/11.png 'Evaluate Model Results') 100 | 101 | ## Task 4: Evaluate Two-Class Logistic Regression Performance 102 | 103 | 1. Scroll down to review model performance metrics for `Two-Class Logistic Regression`. Observe that the **Precision** value is around **0.3**. 104 | 105 | ![Image shows the evaluate model result visualization dialog for Two-Class Logistic Regression.](images/12.png 'Two-Class Logistic Regression Performance') 106 | 107 | ## Task 5: Conclusion 108 | 109 | 1. Based on the primary performance metric, `Precision`, it shows that the `Two-Class Boosted Decision Tree` algorithm outperforms the `Two-Class Logistic Regression` algorithm. 110 | 111 | # Next Steps 112 | Congratulations! You have trained and compared performance of two different classification machine learning models. 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It supports Azure Machine Learning compute, GPU or CPU. Machine Learning designer also supports publishing models as web services on Azure Kubernetes Service that can easily be consumed by other applications. 4 | 5 | In this lab, we will be compare the performance of two different multiclass classification approaches: `Two-Class Support Vector Machine` used with `One-vs-All Multiclass` module vs `Multiclass Decision Forest`. We will apply the two approaches for the letter recognition problem and compare their performance. We will do all of this from the Azure Machine Learning designer without writing a single line of code. 6 | 7 | # Exercise 1: Create Training Pipeline 8 | 9 | ## Task 1: Open Sample 12: Multiclass Classification - Letter Recognition 10 | 11 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 12 | 13 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 14 | 15 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 16 | 17 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 18 | 19 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 20 | 21 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 22 | 23 | 4. From the studio, select **Designer, Show more samples**. 24 | 25 | ![Image highlights the steps to open the sample pipeline, Sample 12: Multiclass Classification - Letter Recognition.](images/01_1.png 'Samples Page') 26 | 27 | 5. Select **Sample 12: Multiclass Classification - Letter Recognition**. 28 | 29 | ![Image highlights the steps to open the sample pipeline, Sample 12: Multiclass Classification - Letter Recognition.](images/01_2.png 'Samples Page') 30 | 31 | ## Task 2: Setup Compute Target 32 | 33 | 1. In the settings panel on the right, select **Select compute target**. 34 | 35 | ![Image highlights the link to select to open the setup compute target editor.](images/02.png 'Setup Compute Target') 36 | 37 | 2. In the `Set up compute target` editor, select the available compute, and then select **Save**. 38 | 39 | >> Note: If you are facing difficulties in accessing pop-up windows or buttons in the user interface, please refer to the Help section in the lab environment. 40 | 41 | ![Image shows how to select the existing compute target named qs-compute.](images/03.png 'Setup Compute Target') 42 | 43 | # Exercise 2: Submit Training Pipeline 44 | 45 | ## Task 1: Create Experiment and Submit Pipeline 46 | 47 | 1. Select **Submit** to open the `Setup pipeline run` editor. 48 | 49 | ![Image shows where to select the submit button to open the setup pipeline run editor.](images/07.png 'Submit Pipeline') 50 | 51 | > Please note that the button name in the UI is changed from **Run** to **Submit**. 52 | 53 | 2. In the `Setup pipeline run editor`, select **Experiment, Create new** and provide `New experiment name:` **letter-recognition**, and then select **Submit**. 54 | 55 | ![Image shows how to provide the experiment name in the setup pipeline run editor and start the pipeline run.](images/08.png 'Submit Pipeline') 56 | 57 | 3. Wait for pipeline run to complete. It will take around **10 minutes** to complete the run. 58 | 59 | 4. While you wait for the model training to complete, you can learn more about the `One-vs-All Multiclass` module used in this lab by selecting [One-vs-All Multiclass](https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/one-vs-all-multiclass). 60 | 61 | # Exercise 3: Compare Model Performance 62 | 63 | ## Task 1: Open Evaluation Results 64 | 65 | 1. Select **Evaluate Model, Outputs, Visualize** to open the `Evaluate Model result visualization` dialog. 66 | 67 | ![Image shows how to open the evaluate model result visualization dialog.](images/09.png 'Evaluate Model Results') 68 | 69 | ## Task 2: Compare Performance Metrics 70 | 71 | 1. Select the regression performance metric **Overall_Accuracy** and compare performance of the two algorithms: `Two-Class Support Vector Machine` and `Multiclass Decision Forest`. 72 | 73 | ![Image shows the evaluate model result visualization dialog.](images/10.png 'Compare Performance Metrics') 74 | 75 | ## Task 3: Conclusion 76 | 77 | 1. The `Two-Class Support Vector Machine` algorithm is extended for multiclass classification problem by using the `One-vs-All Multiclass` module. 78 | 79 | 2. As you can observe that the native multiclass algorithm `Multiclass Decision Forest` outperforms the `Two-Class Support Vector Machine` across all key performance metrics. 80 | 81 | 3. One recommendation for next steps is to increase the `Number of iterations` parameter for the `Two-Class Support Vector Machine` module to an higher value like **100** and observe its impact on the performance metrics. 82 | 83 | # Next Steps 84 | Congratulations! You have trained and compared performance of two different multiclass classification machine learning models. 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gives you a cloud-based interactive, visual workspace that you can use to easily and quickly prep data, train and deploy machine learning models. It supports Azure Machine Learning compute, GPU or CPU. Machine Learning designer also supports publishing models as web services on Azure Kubernetes Service that can easily be consumed by other applications. 4 | 5 | In this lab, we will be compare the performance of two regression algorithms: `Boosted Decision Tree Regression` and `Neural Net Regression` for predicting automobile prices. We will do all of this from the Azure Machine Learning designer without writing a single line of code. 6 | 7 | # Exercise 1: Create Training Pipeline 8 | 9 | ## Task 1: Open Sample 2: Regression - Automobile Price Prediction (Compare algorithms) 10 | 11 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 12 | 13 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 14 | 15 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 16 | 17 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 18 | 19 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 20 | 21 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 22 | 23 | 4. From the studio, select **Designer, Sample 2: Regression - Automobile Price Prediction (Compare algorithms)**. 24 | 25 | ![Image highlights the steps to open the sample pipeline, Sample 2: Regression - Automobile Price Prediction (Compare algorithms).](images/01.png 'Pipeline Authoring Editor') 26 | 27 | ## Task 2: Setup Compute Target 28 | 29 | 1. In the settings panel on the right, select **Select compute target**. 30 | 31 | ![Image highlights the link to select to open the setup compute target editor.](images/02.png 'Setup Compute Target') 32 | 33 | 2. In the `Set up compute target` editor, select the available compute, and then select **Save**. 34 | 35 | >> Note: If you are facing difficulties in accessing pop-up windows or buttons in the user interface, please refer to the Help section in the lab environment. 36 | 37 | ![Image shows how to select the existing compute target named qs-compute.](images/03.png 'Setup Compute Target') 38 | 39 | ## Task 3: Delete Pipeline Modules 40 | 41 | 1. From the `right-hand-side` of the pipeline, select the **Decision Forest Regression module** and then select the **Delete Icon**. 42 | 43 | ![Image shows how to delete an existing module.](images/04.png 'Delete Module') 44 | 45 | ## Task 4: Setup the Neural Net Regression Module 46 | 47 | 1. Select **Machine Learning Algorithms** section in the left navigation. Follow the steps outlined below: 48 | 49 | 1. Select the **Neural Net Regression** prebuilt module 50 | 51 | 2. Drag and drop the selected module on to the canvas 52 | 53 | 3. Set `Number of hidden nodes` to **1000** 54 | 55 | 4. Set `Learning rate` to **0.0001** 56 | 57 | 5. Set `Number of learning iterations` to **10000** 58 | 59 | 6. Set `Random number seed` to **139** 60 | 61 | 7. Connect the `Neural Net Regression` module to the first input of the `Train Model` module 62 | 63 | ![Image shows the steps to add and configure the Neural Net Regression module.](images/udacity-01a.png 'Neural Net Regression Module') 64 | 65 | # Exercise 2: Submit Training Pipeline 66 | 67 | ## Task 1: Create Experiment and Submit Pipeline 68 | 69 | 1. Select **Submit** to open the `Setup pipeline run` editor. 70 | 71 | ![Image shows where to select the submit button to open the setup pipeline run editor.](images/udacity-02.png 'Submit Pipeline') 72 | 73 | > Please note that the button name in the UI is changed from **Run** to **Submit**. 74 | 75 | 2. In the `Setup pipeline run editor`, select **Experiment, Create new** and provide `New experiment name:` **automobile-price-prediction**, and then select **Submit**. 76 | 77 | ![Image shows how to provide the experiment name in the setup pipeline run editor and start the pipeline run.](images/08.png 'Submit Pipeline') 78 | 79 | 3. Wait for pipeline run to complete. It will take around **10 minutes** to complete the run. 80 | 81 | 4. While you wait for the model training to complete, you can learn more about the evaluation metrics for the regression algorithm used in this lab by selecting [Metrics for regression models](https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/evaluate-model#bkmk_regression). 82 | 83 | # Exercise 3: Compare Model Performance 84 | 85 | ## Task 1: Open Evaluation Results 86 | 87 | 1. Select **Evaluate Model, Outputs, Visualize** to open the `Evaluate Model result visualization` dialog. 88 | 89 | ![Image shows how to open the evaluate model result visualization dialog.](images/udacity-03.png 'Evaluate Model Results') 90 | 91 | ## Task 2: Compare Performance Metrics 92 | 93 | 1. Select the regression performance metric **Root_Mean_Squared_Error** and compare performance of the two algorithms: `Boosted Decision Tree Regression` and `Neural Net Regression`. Note that smaller value for `Root_Mean_Squared_Error` implies better performance. 94 | 95 | ![Image shows the evaluate model result visualization dialog.](images/10.png 'Compare Performance Metrics') 96 | 97 | ## Task 3: Conclusion 98 | 99 | 1. Based on the performance metric, `Root_Mean_Squared_Error`, it shows that the `Boosted Decision Tree Regression` algorithm outperforms the `Neural Net Regression` algorithm. One recommendation for next steps is to tune the hyperparameters for the `Neural Net Regression` module to see if we can improve its performance. 100 | 101 | # Next Steps 102 | Congratulations! You have trained and compared performance of two different regression machine learning models. 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Machine Learning capability in Azure Machine Learning (AML) can be used for the life cycle management of the manufactured vehicles and how AML helps in creation of better vehicle maintenance plans. To accomplish this, you will train a Linear Regression model to predict the number of days until battery failure using Automated Machine Learning available in AML studio. 6 | 7 | ## Exercise 1: Creating a model using automated machine learning 8 | 9 | ### Task 1: Create an automated machine learning experiment using the Portal 10 | 11 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 12 | 13 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 14 | 15 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 16 | 17 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 18 | 19 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 20 | 21 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 22 | 23 | 4. Select **Automated ML** in the left navigation bar. 24 | 25 | ![Select Automated ML](./images/02.png) 26 | 27 | 5. Select **New automated ML run** to start creating a new experiment. 28 | 29 | ![New automated ML run](./images/03.png) 30 | 31 | 6. Select **Create dataset** and choose the **From web files** option from the drop-down. 32 | 33 | ![Create dataset from local file](./images/04.png) 34 | 35 | 7. Fill in the training data URL in the `Web URL` field: `https://introtomlsampledata.blob.core.windows.net/data/battery-lifetime/training-formatted.csv`, make sure the name is set to `training-formatted-dataset`, and select **Next** to load a preview of the parsed training data. 36 | 37 | ![Training data web URL](./images/05.png) 38 | 39 | 8. In the `Settings and preview` page, for the `Column headers` field, select `All files have same headers`. Scroll to the right to observe all of the columns in the data. 40 | 41 | ![Reviewing the training data](./images/06.png) 42 | 43 | 9. Select **Next** to check the schema and then confirm the dataset details by selecting **Next** and then **Create** on the confirmation page. 44 | 45 | ![Reviewing the schema of training data](./images/07.png) 46 | 47 | 10. Now you should be able to select the newly created dataset for your experiment. Select the `training-formatted-dataset` dataset and select **Next** to move to the experiment run details page. 48 | 49 | ![Select the dataset](./images/08.png) 50 | 51 | 11. You will now configure the Auto ML run basic settings by providing the following values for the experiment name, target column and training compute: 52 | 53 | - Experiment name: **automlregression** 54 | - Target column: select **Survival_In_Days** 55 | - Select training compute target: : select **qs-compute** 56 | 57 | ![Setup Auto ML experiment basic settings](./images/09.png) 58 | 59 | 12. Select **Next** and select **Regression** in the `Task type and settings` page. 60 | 61 | ![Select Regression task type](./images/10.png) 62 | 63 | 13. Select **View additional configuration settings** to open the advanced settings section. Provide the following settings: 64 | 65 | - Primary metric: **Normalized root mean squared error** 66 | - Exit criterion > Metric score threshold: **0.09** 67 | - Validation > Validation type: **k-fold cross validation** 68 | - Validation > Number of Cross Validations: **5** 69 | - Concurrency > Max concurrent iterations: **1** 70 | 71 | ![Configuring the Advanced Settings as described](./images/11.png) 72 | 73 | 14. Select **Save** and then **Finish** to begin the automated machine learning process. 74 | 75 | ![Start Automate ML run](./images/12.png) 76 | 77 | 15. Wait until the `Run status` becomes **Running** in the `Run Detail page`. 78 | 79 | ![Preparing experiment](./images/13.png) 80 | 81 | ### Task 2: Review the experiment run results 82 | 83 | 1. The experiment will run for about _15 minutes_. While it runs and once it completes, you should check the `Models` tab on the `Run Detail` page to observe the model performance for the primary metric for different runs. 84 | 85 | ![Review run details - model performance metric](./images/14.png) 86 | 87 | 2. In the models list, notice at the top the iteration with the best **normalized root mean square error** score. Note that the normalized root mean square error measures the error between the predicted value and actual value. In this case, the model with the lowest normalized root mean square error is the best model. 88 | 89 | ![Review run details - table view](./images/15.png) 90 | 91 | 3. Select **Experiments** on the left navigation pane and select the experiment `automlregression` to see the list of available runs. 92 | 93 | ![Open experiment runs table](./images/16.png) 94 | 95 | 4. Select the option to **Include child runs** to be able to examine model performance for the primary metric of different runs. By default, the left chart describes the `normalized_median_absolute_error` value for each run. Select the pen icon on the right corner of the `normalized_median_absolute_error` chart to configure the `normalized_root_mean_square_error` metric representation. 96 | 97 | ![Review runs - chart view](./images/17.png) 98 | 99 | ## Next Steps 100 | 101 | Congratulations! You have trained a simple time-series forecasting model using automated machine learning in the visual interface. 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Now it is time to gain some hands-on experience by putting a compute instance to work. 6 | 7 | # Overview 8 | 9 | In this lab, you learn the foundational design patterns in Azure Machine Learning, and train a simple scikit-learn model based on the diabetes data set. After completing this lab, you will have the practical knowledge of the SDK to scale up to developing more-complex experiments and workflows. 10 | 11 | In this tutorial, you learn the following tasks: 12 | 13 | > * Connect your workspace and create an experiment 14 | > * Load data and train a scikit-learn model 15 | 16 | ## Exercise 1: Run the Notebook for this Lab 17 | 18 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 19 | 20 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 21 | 22 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 23 | 24 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 25 | 26 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 27 | 28 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 29 | 30 | 4. From the studio, navigate to **Compute**. Next, for the available Compute Instance, under Application URI select `Jupyter`. Be sure to select `Jupyter` and not `JupterLab`. 31 | 32 | ![Image highlights the steps to launch Jupyter from the Compute Instance.](images/02.png "Launch Jupyter from Compute Instance") 33 | 34 | 5. From within the Jupyter interface, select **New, Terminal**. 35 | 36 | ![Image highlights the steps to launch terminal from the Jupyter interface.](images/05.png "Launch Terminal") 37 | 38 | 6. In the new terminal window run the following command and wait for it to finish: 39 | 40 | `git clone https://github.com/solliancenet/udacity-intro-to-ml-labs.git` 41 | 42 | ![Image highlights the steps to clone the Github repo.](images/06.png "Clone Github Repo") 43 | 44 | 7. From within the Jupyter interface, navigate to directory `udacity-intro-to-ml-labs/aml-visual-interface/lab-19/notebook` and open `1st-experiment-sdk-train-model.ipynb`. This is the Python notebook you will step through executing in this lab. 45 | 46 | ![Image highlights the steps to open the notebook.](images/07.png 'Opening the notebook') 47 | 48 | 8. Follow the instructions within the notebook to complete the lab. 49 | 50 | 9. After completing the notebook, navigate back to the **Notebook** folder, then select the newly created **outputs** sub-folder. Here you will see the trained models (`*.pkl` files) generated by the last cell you executed. In addition, the serialized model is uploaded to each run. This allows you to download the model file from the run in the portal as an alternative to downloading them from this folder. 51 | 52 | ![The serialized models are displayed in the outputs sub-folder.](images/model-files1.png "Model files") 53 | 54 | # Next Steps 55 | 56 | Congratulations! You have just learned how to use the Jupyter application on a compute instance to train a model. 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These runs were created within the experiment you created at the beginning of the notebook. Because of this, Azure Machine Learning logged the details so you can review the result of each run and see how the alpha value is different between the them. 6 | 7 | # Overview 8 | 9 | In this lab, you view the experiments and runs executed by a notebook. In the first part of the lab, you will use a notebook to create and run the experiments. In the second part of the lab, you will navigate to the **Experiments** blade in Azure Machine Learning Studio. Here you see all the individual runs in the experiment. Any custom-logged values (alpha_value and rmse, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric. 10 | 11 | When training models at scale over hundreds and thousands of separate runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time. 12 | 13 | ## Exercise 1: Run the Notebook for this Lab 14 | 15 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 16 | 17 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 18 | 19 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 20 | 21 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 22 | 23 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 24 | 25 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 26 | 27 | 4. From the studio, navigate to **Compute**. Next, for the available Compute Instance, under Application URI select `Jupyter`. Be sure to select `Jupyter` and not `JupterLab`. 28 | 29 | ![Image highlights the steps to launch Jupyter from the Compute Instance.](images/02.png "Launch Jupyter from Compute Instance") 30 | 31 | 5. From within the Jupyter interface, select **New, Terminal**. 32 | 33 | ![Image highlights the steps to launch terminal from the Jupyter interface.](images/05.png "Launch Terminal") 34 | 35 | 6. In the new terminal window run the following command and wait for it to finish: 36 | 37 | `git clone https://github.com/solliancenet/udacity-intro-to-ml-labs.git` 38 | 39 | ![Image highlights the steps to clone the Github repo.](images/06.png "Clone Github Repo") 40 | 41 | 7. From within the Jupyter interface, navigate to directory `udacity-intro-to-ml-labs/aml-visual-interface/lab-20/notebook` and open `1st-experiment-sdk-train-model.ipynb`. This is the Python notebook you will step through executing in this lab. 42 | 43 | ![Image highlights the steps to open the notebook.](images/07.png 'Opening the notebook') 44 | 45 | 8. Follow the instructions within the notebook to complete the exercise. 46 | 47 | ## Exercise 2: Open Experiments in the portal 48 | 49 | 1. Within Azure Machine Learning Studio, select **Experiments** in the left-hand menu, then select the **diabetes-experiment** submitted by the notebook you executed in the previous lab (19). 50 | 51 | ![The Experiments blade is displayed and the diabetes experiment is highlighted.](images/experiments.png "Experiments") 52 | 53 | 2. Here you can view details about the experiment and each of its runs, which created a new version of the model. 54 | 55 | ![The experiment details are displayed.](images/diabetes-experiment.png "diabetes-experiment") 56 | 57 | 3. Select **Edit table** in the top toolbar. In the Edit table dialog that appears, add the **End time** and **Start time** columns to the Selected columns list, then select **Save**. 58 | 59 | ![The start time and end time columns are moved to the selected columns list.](images/edit-table.png "Edit table dialog") 60 | 61 | Depending on your screen resolution, you might need to scroll down the table to see the bottom horizontal scrollbar. When you scroll all the way to the right, you will see the new columns you added. 62 | 63 | ![Thew new columns appear all the way on the right-hand side of the table.](images/added-columns.png "Added columns") 64 | 65 | 4. Select either the **Run number** *or* the **Run ID** of one of the runs to view its details. Both links on a run display the same dialog. 66 | 67 | ![The Run number and Run ID links are highlighted.](images/run-links.png "Run links") 68 | 69 | 5. The **Details** tab shows you more detailed information about each run, including the run time and metrics. 70 | 71 | ![The details tab is displayed.](images/run-details.png "Run details") 72 | 73 | 6. Select the **Outputs + logs** tab. You see the `.pkl` file for the model that was uploaded to the run during each training iteration. This lets you download the model file rather than having to retrain it manually. 74 | 75 | ![The model file is highlighted.](images/run-outputs.png "Outputs + logs") 76 | 77 | # Next Steps 78 | 79 | Congratulations! You have just learned how to use the Azure Machine Learning SDK to help you explain what influences the predictions a model makes. 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The model training process can be very compute-intensive, with training times spanning across many hours, days, or weeks depending on the amount of data, type of algorithm used, and other factors. A trained model, on the other hand, is used to make decisions on new data quickly. In other words, it infers things about new data it is given based on its training. Making these decisions on new data on-demand is called real-time inferencing. 6 | 7 | # Overview 8 | 9 | In this lab, you learn how to deploy a trained model that can be used as a webservice, hosted on an Azure Kubernetes Service (AKS) cluster. This process is what enables you to use your model for real-time inferencing. 10 | 11 | The Azure Machine Learning designer simplifies the process by enabling you to train and deploy your model without writing any code. 12 | 13 | ## Exercise 1: Open a sample training pipeline 14 | 15 | ### Task 1: Open the pipeline authoring editor 16 | 17 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 18 | 19 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 20 | 21 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 22 | 23 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 24 | 25 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 26 | 27 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 28 | 29 | 4. From the studio, select **Designer** in the left-hand menu. Next, select **Sample 1: Regression - Automobile Price Prediction (Basic)** under the **New pipeline** section. This will open a `visual pipeline authoring editor`. 30 | 31 | ![The Sample 1 pipeline is selected.](images/new-pipeline.png "Designer: New pipeline") 32 | 33 | ### Task 2: Setup the compute target 34 | 35 | 1. In the settings panel on the right, select **Select compute target**. 36 | 37 | ![The select compute target link is highlighted.](images/select-compute-target-link.png "Select compute target link") 38 | 39 | 2. In the `Set up compute target` editor, select the existing compute target, then select **Save**. 40 | 41 | >> Note: If you are facing difficulties in accessing pop-up windows or buttons in the user interface, please refer to the Help section in the lab environment. 42 | 43 | ![Select the existing compute target, then select Save.](images/set-up-compute-target.png "Set up compute target") 44 | 45 | ### Task 3: Create a new experiment and submit the pipeline 46 | 47 | 1. Select **Submit** to open the `Set up pipline run` editor. 48 | 49 | ![The Submit button is highlighted.](images/run-button.png "Submit") 50 | 51 | > Please note that the button name in the UI is changed from **Run** to **Submit**. 52 | 53 | 2. In the `Setup pipeline run editor`, select **Experiment, Create new** and provide `New experiment name`: **designer-run**, and then select **Submit**. 54 | 55 | ![The dialog is displayed with the previously described values.](images/set-up-pipeline-run.png "Set up pipeline run") 56 | 57 | 3. Wait for the pipeline run to complete. It will take around **10 minutes** to complete the run. 58 | 59 | ## Exercise 2: Real-time inference pipeline 60 | 61 | ### Task 1: Create pipeline 62 | 63 | 1. Select **Create inference pipeline**, then select **Real-time inference pipeline** from the list to create a new inference pipeline. 64 | 65 | ![Select the real-time inference pipeline option.](images/new-real-time-inference-pipeline.png "Real-time inference pipeline") 66 | 67 | ### Task 2: Submit the pipeline 68 | 69 | 1. Select **Submit** to open the `Set up pipeline run` editor. 70 | 71 | ![Select the Submit button.](images/run-inference-pipeline.png "Submit") 72 | 73 | > Please note that the button name in the UI is changed from **Run** to **Submit**. 74 | 75 | 2. In the `Setup pipeline run` editor, select **Select existing**, then select the experiment you created in an earlier step: **designer-run**. Select **Submit** to start the pipeline. 76 | 77 | ![Select your previous experiment.](images/set-up-inference-pipeline-run.png "Set up pipeline run") 78 | 79 | 3. Wait for pipeline run to complete. It will take around **7 minutes** to complete the run. 80 | 81 | ## Exercise 3: Deploy web service on Azure Kubernetes Service compute 82 | 83 | ### Task 1: Deploy the web service 84 | 85 | 1. After the inference pipeline run is finished, select **Deploy** to open the `Set up real-time endpoint` editor. 86 | 87 | ![Select Deploy to open the editor.](images/deploy-web-service.png "Deploy") 88 | 89 | 2. In the `Set up real-time endpoint` editor, select your **existing compute target**, then select **Deploy**. 90 | 91 | ![The dialog shows the selected compute target.](images/set-up-real-time-endpoint.png "Set up real-time endpoint") 92 | 93 | 3. Wait for the deployment to complete. The status of the deployment can be observed above the `Pipeline Authoring Editor`. 94 | 95 | ![Deployment completed dialog is shown.](images/deploy-succeeded.png "Deploy - Succeeded") 96 | 97 | ### Task 2: Review deployed web service 98 | 99 | 1. To view the deployed web service, select the **Endpoints** section in your Azure Portal Workspace. 100 | 101 | 2. Select the deployed web service: **sample-1-regression---automobile** to open the deployment details page. 102 | 103 | ![The deployed service is highlighted within the Endpoints blade.](images/endpoints.png "Endpoints") 104 | 105 | *Note: you have to select the text of the service name to open the deployment details page* 106 | 107 | ### Task 3: Review how to consume the deployed web service 108 | 109 | 1. Select the **Consume** tab to observe the following information: 110 | 111 | 1. `Basic consumption info` displays the **REST endpoint**, **Primary key**, and **Secondary key**. 112 | 113 | 2. `Consumption option` shows code samples in **C#**, **Python**, and **R** on how to call the endpoint to consume the webservice. 114 | 115 | ![The Consume tab is displayed.](images/consume.png "Service details: Consume") 116 | 117 | # Next Steps 118 | 119 | Congratulations! You have just learned how to train and deploy a model to an Azure Kubernetes Service (AKS) cluster for real-time inferencing. 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comprehensive set of a capabilities that you can use directly within a python notebook or python code including: 8 | 9 | - Creating a **Workspace** that acts as the root object to organize all artifacts and resources used by Azure Machine Learning. 10 | - Creating **Experiments** in your Workspace that capture versions of the trained model along with any desired model performance telemetry. Each time you train a model and evaluate its results, you can capture that run (model and telemetry) within an Experiment. 11 | - Creating **Compute** resources that can be used to scale out model training, so that while your notebook may be running in a lightweight container in Azure Notebooks, your model training can actually occur on a powerful cluster that can provide large amounts of memory, CPU or GPU. 12 | - Using **Automated Machine Learning (AutoML)** to automatically train multiple versions of a model using a mix of different ways to prepare the data and different algorithms and hyperparameters (algorithm settings) in search of the model that performs best according to a performance metric that you specify. 13 | - Packaging a Docker **Image** that contains everything your trained model needs for scoring (prediction) in order to run as a web service. 14 | - Deploying your Image to either Azure Kubernetes or Azure Container Instances, effectively hosting the **Web Service**. 15 | 16 | # Overview 17 | 18 | In this lab, you start with a model that was trained using Automated Machine Learning. Learn how to use the Azure ML Python SDK to register, package, and deploy the trained model to Azure Container Instances (ACI) as a scoring web service. Finally, test the deployed model (1) by make direct calls on service object, (2) by calling the service end point (Scoring URI) over http. 19 | 20 | ## Exercise 1: Run the Notebook for this Lab 21 | 22 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 23 | 24 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 25 | 26 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 27 | 28 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 29 | 30 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 31 | 32 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 33 | 34 | 4. From the studio, navigate to **Compute**. Next, for the available Compute Instance, under Application URI select `Jupyter`. Be sure to select `Jupyter` and not `JupterLab`. 35 | 36 | ![Image highlights the steps to launch Jupyter from the Compute Instance.](images/02.png "Launch Jupyter from Compute Instance") 37 | 38 | 5. From within the Jupyter interface, select **New, Terminal**. 39 | 40 | ![Image highlights the steps to launch terminal from the Jupyter interface.](images/05.png "Launch Terminal") 41 | 42 | 6. In the new terminal window run the following command and wait for it to finish: 43 | 44 | `git clone https://github.com/solliancenet/udacity-intro-to-ml-labs.git` 45 | 46 | ![Image highlights the steps to clone the Github repo.](images/06.png "Clone Github Repo") 47 | 48 | 7. From within the Jupyter interface, navigate to directory `udacity-intro-to-ml-labs/aml-visual-interface/lab-22/notebook` and open `deployment-with-AML.ipynb`. This is the Python notebook you will step through executing in this lab. 49 | 50 | ![Image highlights the steps to open the notebook.](images/07.png 'Opening the notebook') 51 | 52 | 8. In the Setup portion of the notebook, you will be asked to provide values for `subscription_id`, `resource_group`, `workspace_name`, and `workspace_region`. To find these, open your Azure Machine Learning workspace in the Azure portal and copy the values as shown: 53 | 54 | ![The Azure Machine Learning workspace is shown with arrows pointing from the values to the parameters in the notebook cell.](images/aml-values.png "Azure Machine Learning values") 55 | 56 | 9. Follow the instructions within the notebook to complete the lab. 57 | 58 | # Next Steps 59 | 60 | Congratulations! You have just learned how to use the Jupyter application on a compute instance to deploy a trained model to Azure Container Instances (ACI) for real-time inferencing. You can now return to the Udacity portal to continue with the lesson. 61 | -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/00.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/00.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/01a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/01a.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/02.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/02.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/05.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/05.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/06.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/06.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/07.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/07.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/aml-values.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/aml-values.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/new_compute_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/new_compute_1.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/new_compute_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/new_compute_2.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/notebook-link.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/notebook-link.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/images/upload.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/solliancenet/udacity-intro-to-ml-labs/d1477988107e52ea8b7ff2d2cd25b69930f415a6/aml-visual-interface/lab-22/images/upload.png -------------------------------------------------------------------------------- /aml-visual-interface/lab-22/notebook/automl_dependencies.yml: -------------------------------------------------------------------------------- 1 | name: project_environment 2 | dependencies: 3 | # The python interpreter version. 4 | # Currently Azure ML only supports 3.5.2 and later. 5 | - python=3.6.2 6 | 7 | - pip: 8 | - azureml-train-automl-runtime==1.10.0 9 | - inference-schema 10 | - azureml-explain-model==1.10.0 11 | - azureml-defaults==1.10.0 12 | - numpy>=1.16.0,<1.17.0 13 | - pandas>=0.21.0,<=0.23.4 14 | - scikit-learn>=0.19.0,<=0.20.3 15 | - py-xgboost<=0.90 16 | - fbprophet==0.5 17 | - psutil>=5.2.2,<6.0.0 18 | - joblib==0.15.1 19 | channels: 20 | - anaconda 21 | - conda-forge 22 | -------------------------------------------------------------------------------- /aml-visual-interface/lab-23/README.md: -------------------------------------------------------------------------------- 1 | # Explaining models 2 | 3 | ## Model interpretability with Azure Machine Learning service 4 | Machine learning interpretability is important in two phases of machine learning development cycle: 5 | 6 | * During training: Model designers and evaluators require interpretability tools to explain the output of a model to stakeholders to build trust. They also need insights into the model so that they can debug the model and make decisions on whether the behavior matches their objectives. Finally, they need to ensure that the model is not biased. 7 | * During inferencing: Predictions need to be explainable to the people who use your model. For example, why did the model deny a mortgage loan, or predict that an investment portfolio carries a higher risk? 8 | 9 | The [Azure Machine Learning Interpretability Python SDK](https://docs.microsoft.com/en-us/python/api/azureml-explain-model/?view=azure-ml-py) incorporates technologies developed by Microsoft and proven third-party libraries (for example, SHAP and LIME). The SDK creates a common API across the integrated libraries and integrates Azure Machine Learning services. Using this SDK, you can explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. 10 | 11 | # Overview 12 | In this lab, we will be using a subset of NYC Taxi & Limousine Commission - green taxi trip records available from [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/). The data is enriched with holiday and weather data. We will use data transformations and the GradientBoostingRegressor algorithm from the scikit-learn library to train a regression model to predict taxi fares in New York City based on input features such as, number of passengers, trip distance, datetime, holiday information and weather information. 13 | 14 | The primary goal of this quickstart is to explain the predictions made by our trained model with the various [Azure Model Interpretability](https://docs.microsoft.com/en-us/azure/machine-learning/service/machine-learning-interpretability-explainability) packages of the Azure Machine Learning Python SDK. 15 | 16 | ## Exercise 1: Run the Notebook for this Lab 17 | 18 | 1. In [Azure portal](https://portal.azure.com/), open the available machine learning workspace. 19 | 20 | 2. Select **Launch now** under the **Try the new Azure Machine Learning studio** message. 21 | 22 | ![Launch Azure Machine Learning studio.](images/01a.png 'Launch AML') 23 | 24 | 3. When you first launch the studio, you may need to set the directory and subscription. If so, you will see this screen: 25 | 26 | ![Launch Azure Machine Learning studio.](images/00.png 'Launch AML') 27 | 28 | > For the directory, select **Udacity** and for the subscription, select **Azure Sponsorship**. For the machine learning workspace, you may see multiple options listed. **Select any of these** (it doesn't matter which) and then click **Get started**. 29 | 30 | 4. From the studio, navigate to **Compute**. Next, for the available Compute Instance, under Application URI select `Jupyter`. Be sure to select `Jupyter` and not `JupterLab`. 31 | 32 | ![Image highlights the steps to launch Jupyter from the Compute Instance.](images/02.png "Launch Jupyter from Compute Instance") 33 | 34 | 5. From within the Jupyter interface, select **New, Terminal**. 35 | 36 | ![Image highlights the steps to launch terminal from the Jupyter interface.](images/05.png "Launch Terminal") 37 | 38 | 6. In the new terminal window run the following command and wait for it to finish: 39 | 40 | `git clone https://github.com/solliancenet/udacity-intro-to-ml-labs.git` 41 | 42 | ![Image highlights the steps to clone the Github repo.](images/06.png "Clone Github Repo") 43 | 44 | 7. From within the Jupyter interface, navigate to directory `udacity-intro-to-ml-labs/aml-visual-interface/lab-23/notebook` and open `interpretability-with-AML.ipynb`. This is the Python notebook you will step through executing in this lab. 45 | 46 | ![Image highlights the steps to open the notebook.](images/07.png 'Opening the notebook') 47 | 48 | 8. Follow the instructions within the notebook to complete the lab. 49 | 50 | # Next Steps 51 | Congratulations! You have just learned how to use the Azure Machine Learning SDK to help you explain what influences the predictions a model makes. 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