The Azure Machine Learning Studio enables Data Scientists and Developers of different skillsets to harness the power of Azure to manage their Machine Learning experiments. Scalable compute resources can be used to process data science tasks without any up-front hardware investment. Pipelines allow ML engineers to bring CI/CD's operational efficiency to their day-to-day tasks. The Azure ML Studio gives a no-code experience through the Designer tool and only-code interaction through Azure Notebooks.
In this lab, you will explore Azure Machine Learning Studio and use the Designer tool to create a Machine Learning Pipeline and deploy it as a web service. This lab involves running two pipelines that can each take up to 15 minutes to complete. Please make sure you have enough time available before starting this lab.
Upon completion of this lab you will be able to:
This lab is intended for:
You should be familiar with:
April 6th, 2023 - Updated the instructions and screenshots to reflect the latest UI
October 17th, 2022 - Updated screenshots & instructions due to UI updates
December 16th, 2021 - Provided a workaround for when the real-time inference endpoint deployment times out
November 8, 2021 - Upgraded compute unit in ML studio
September 30, 2021 - Updated lab instructions to fix the access issue with the Machine learning studio
Luke is a Site Reliability Engineer at Microsoft. His background is infrastructure development using Terraform and in 2021 he was awarded the HashiCorp Ambassador award. He is an Azure DevOps Engineer Expert, Azure Administrator Associate, and HashiCorp Certified - Terraform Associate.