Link Azure ML workspace and Azure Databricks workspace

  • Azure Machine Learning gives us a workbench to manage the end-to-end Machine Learning lifecycle that can be used by coding & non-coding data scientists
  • Databricks gives us a scalable compute environment: if we want to run a big data machine learning job, it should run on Databricks

In this article, we will look at how Databricks can be used as a compute environment to run machine learning pipelines created with the Azure ML’s Python SDK. This topic is something that i found a little hard to follow within Azure ML documentation. By using a Databricks compute, big data can be efficiently processed in your ML projects. At the same time you can give access to business users to create , manage and run machine learning experiments without worrying about coding and focusing on the business need.

As the starting step we need to create a databricks workspace in the Azure portal and link this workspace to an Azure ML workspace. This is a very critical step as I struggled to link an excising ML workspace to a databricks workspace. The only thing that worked for me was to use the link function in the Azure databricks workspace home page. In the screenshot below I used a Databricks premium workspace.

Once you click on “Link Azure ML workspace” you will see the screen shown below. Here make sure it is the same resource group and region as the databricks workspace.

Next we will create a cluster in the databricks workspace. The runtime I chose was 7.1(includes Apache Spark 3.0.0, Scala 2.12), Cluster Mode – Standard, Worker Type – Standard_D12_v2 28 GB , 4 cores, 1 DBU, Driver Type – Standard_D12_v2 28 GB , 4 cores, 1 DBU. Make sure this runtime is not ML

On this cluster install the azureml-sdk[databricks] library (Type PYPI)

Now create a new python notebook and attach it to the cluster created. Follow the code below to authenticate to the ML workspace.

%pip install –upgrade –force-reinstall -r https://aka.ms/automl_linux_requirements.txt

import azureml.core
print("SDK Version:", azureml.core.VERSION)

subscription_id = "your sub id" #you should be owner or contributor
resource_group = "azure ml workspace resource group name" #you should be owner or contributor
workspace_name = "azure ml workspace name" #your workspace name
workspace_region = "azure ml workspace region" #your region

from azureml.core import Workspace
from azureml.core.authentication import InteractiveLoginAuthentication
interactive_auth = InteractiveLoginAuthentication()
ws = Workspace.get(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group)

ws.get_details()

Next I will show you how to create an auto ml experiment and deploy a model both in the Azure ML workspace and in Azure Databricks using python code.

Published by Narayan Sujay Somasekhar

• 12+ years of experience leading the build of BI and Cloud Data Platform solutions using cloud technologies such as Snowflake, Azure Synapse, Databricks and AWS Redshift. • Over 8+ years as a Data Analytics and Engineering practice leader with demonstrated history of working with management consulting firms across Tax & Accounting, Finance, Power & Utility industry. • Experience in managing the team roadmap, and delivering actionable data insights to sales, product, marketing, and senior leadership. • Strong background in Data Technology Solutions delivery & Data Automation for business processes using various tools. • Expertise in bringing Data-Driven IT Strategic Planning to align metrics, communicate data changes across reporting, Enterprise Data Warehouses, Data Lakes and Customer Relationship Managements Systems. • Experienced working with cross functional teams, Data Scientists/Analysts and Business Managers in building Data Science and Data Engineering practice from the ground up. • Experienced in Designing and implementing NLP solutions with focus on sentiment analysis, opinion mining, key phase extraction using Azure Cognitive Services and Amazon Comprehend • Extensive programming experience with SQL, Python, C#, R, and Scala.

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