Microsoft Purview is a unified data governance service that helps you manage and govern data across different sources.

Machine Learning project life cycle involves many steps to transform raw data into insights. This process usually requires individuals with different roles/skillsets across multiple teams to collaborate effectively. Microsoft Purview helps simplify this complex process by providing an end-to-end lineage of ML entities and processes to enable better collaboration, auditing and debugging capabilities.

This solution accelerator helps developers with the resources needed to build an end-to-end lineage in Purview for Machine Learning scenarios.

To use this solution accelerator, you will need access to an Azure subscription. While not required, a prior understanding of Microsoft Purview, Azure Synapse Analytics and Machine Learning will be helpful.

For additional training and support, please see:

1. Microsoft Purview
2. Azure Synapse Analytics
3. Azure Machine Learning

Start by deploying the required resources to Azure. The button below will deploy Microsoft Purview, Azure Synapse Analytics, Azure Machine Learning and its related resources:

If you prefer to setup manually, you need to deploy Microsoft Purview, Azure Synapse Analytics, Azure Machine Learning.

Note: To minimize Azure costs, consider deleting the Purview instance at the end of this exercise if you do not plan to use this instance actively.

Clone or download this repository and navigate to the project's root directory.

Create a service principal

Configure your Purview catalog to trust the service principal

Create a service principal

1. From the Azure portal, select your AML workspace

2. select Access Control (IAM)

3. Select Add, Add Role Assignment to open the Add role assignment page

   3.1 For the Role type in Contributor

   3.2 For Assign access to leave the default, User, group, or service principal

   3.2 For Select enter the name of the previosly created service principal in step 3.1 and then click on their name in the results pane

   3.3 Click on Save You've now configured the service principal as a contributor on Azure Machine Learning resource.

Before you can upload assests to the Synapse Workspace you will need to add your IP address:

1. Go to the Synapse resouce you created in the previous step
2. Navigate to Firewalls under Security on the left hand side of the page
3. At the top of the screen click + Add client IP
4. Your IP address should now be visable in the IP list

In order to perform the necessary actions in Synapse workspace, you will need to grant more access.

1. Go to the Azure Data Lake Storage Account created above
2. Go to the Access Control (IAM) > + Add > Add role assignment
3. Now click the Role dropdown and select Storage Blob Data Contributor
   • Search for your username and add
4. Click Save at the bottom

Learn more

1. Launch the Synapse workspace Synapse Workspace
2. Select the subscription and workspace name you are using for this solution accelerator
3. In Synapse Studio, navigate to the Data Hub
4. Select Linked
5. Under the category Azure Data Lake Storage Gen2 you'll see an item with a name like xxxxx(xxxxx- Primary)
6. Select the container named data (Primary)
7. Create a new folder creditriskdata
8. Select Upload and select loan.csv and borrower.csv files downloaded from Data folder

1. Setting up authentication for a scan

2. Register and scan adls gen2

select only the creditriskdata folder while creating the scan.

Wait for scan run status to change to Completed before running next step.

1. Launch the Synapse workspace Synapse Workspace

2. Select the subscription and workspace name you are using for this solution accelerator

3. Go to the Manage tab in the Synapse workspace and click on the Apache Spark pools

   • 

4. Click ... on the deployed Spark Pool and select Packages

5. Click Upload and select requirements.txt from the cloned repo and click Apply

   • 

6. Go to Develop, click the +, and click Import to select all notebooks from the repository's /SynapseNotebooks/ folder

7. For each of the notebooks, select Attach to > spark1 in the top dropdown

8. Update Purview Tenant, Client Id and Secret from step 2.1 in 01_Authenticate_to_Purview_AML.ipynb

9. Update Azure Machine Learning Tenant, Client Id and Secret from step 3.1 in 01_Authenticate_to_Purview_AML.ipynb

10. Update account_name variable to your ADLS in 04_Create_CreditRisk_Experiment.ipynb

11. Click Publish all to publish the notebook changes

12. Run the following notebook:

    • 04_Create_CreditRisk_Experiment.ipynb (This notebook runs other notebooks you imported)

1. Launch Purview Studio
2. Click on Browse Assets
3. Click on Custom Model and select the model we created from running notebooks in Step 7
4. Click on Lineage to see Machine Learning process Lineage

1. Launch the Azure Machine Learning studio AML Studio
2. Select the subscription and workspace name you are using for this solution accelerator
3. Go to the Notebooks tab in the AML Studio and upload the notebooks and scripts in AML Notebooks folder including Data folder
4. Go to the Compute tab in the AML Studio and click on the Compute Instances
5. Click New and create a new compute instance
6. Click Jupyter and launch the compute instance
7. In the browser window that opens, click the folders to see the notebooks you uploaded in step 9.3
8. Update Purview Tenant, Client Id and Secret from step 2.1 in Authenticate_to_Purview_AML.py
9. Update Azure Machine Learning Tenant, Client Id and Secret from step 3.1 in Authenticate_to_Purview_AML.py
10. Run the following notebooks in order:
    • 01_Create_CreditRisk_AML_Pipeline.ipynb ( Pipeline run might take few minutes so please wait for completion before running the next notebook)
    • 02_Create_CreditRisk_AML_Pipeline_Lineage.ipynb

1. Launch Purview Studio
2. Click on Browse Assets
3. Click on Custom ML Experiment Step and select any step we created from running notebooks in Step 9
4. Click on Lineage to see Machine Learning pipeline Lineage

The architecture diagram below details what you will be building for this Solution Accelerator.

MIT License

Copyright (c) Microsoft Corporation.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE

The following libraries are not explicitly included in this repository, but users who use this Solution Accelerator may need to install them locally and in Azure Synapse and Azure Machine Learning to fully utilize this Solution Accelerator. However, the actual binaries and files associated with the libraries are not included as part of this repository, but they are available for installation via the PyPI library using the pip installation tool.

Libraries: chardet, certifi

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