N a n o d e g r e e p r o g r a m s y L l a b u s

LESSON TWO

Workspaces and the 

Azure ML Studio

•

 Interpret the Azure ML Platform

•

 Explain how to manage and choose compute resources

•

 Summarize the key components of Workspaces and  

   Notebooks

LESSON THREE

Datastores and 

Datasets

• 

Analyze how to manage data

• 

Construct datasets

• 

Compose solutions to manage data drift and deal with 

   sensitive data

LESSON FOUR

Training Models in 

Azure ML

• 

Experiment with the Designer

• 

Develop and manage pipelines

• 

Organize and run hyperparameter experiments 

Project 1

 

Optimizing an ML 

Pipeline in Azure

Throughout the course, we cover many different ways to work with 

data and machine learning. It can be quite challenging to decide 

what method to use - building your own machine learning pipeline, 

leveraging AutoML, hyperparameter tuning, and so on. In this project, 

students use scikit-learn, Hyperdrive, and AutoML to understand the 

costs and benefits of each methodology. First, students will construct 

a pipeline from scikit-learn, using the Azure ML SDK to import data 

from a URL. Then, students will configure a Hyperdrive run for their 

scikit-learn pipeline to find the optimal hyperparameters. Students 

will then use the same dataset for an AutoML run to find an optimal 

model and set of hyperparameters. Finally, students write a README 

documenting their findings and comparing the differences, costs, and 

benefits of the different methods they’ve used. 

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Machine Learning Engineer with Microsoft Azure |  4