The Azure Machine Learning SDK allows data scientists and machine learning engineers to harness the cloud's power in their day-to-day operations. Data science enthusiasts can train their models against scalable compute and GPU clusters on-demand in the cloud, all from the same Jupyter notebook experience.
With the Azure Machine Learning SDK, comes Azure ML pipelines. Machine learning engineers can create a CI/CD approach to their data science tasks by splitting their workflows into pipeline steps. This allows for greater scalability when dealing with large scale data. The separation of functions greatly benefits complex model orchestration as engineers and scientists can focus on one segment at a time. It also provides cost savings by enabling different levels of compute for each step.
In this lab, you will dive into Azure Notebooks and launch a Jupyter notebook to build an Azure ML pipeline that ingests data, trains a model, and deploys a web service.
Upon completion of this lab you will be able to:
This lab is intended for:
You should be familiar with:
September 8th, 2022 - Update notebook to use the latest
azureml notebook environment
August 26th, 2021 - Update notebook code to not use deprecated classes
August 25h, 2021 - Updated instructions to reflect the latest Azure Machine Learning experience
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.