Lab
A Cloud Guru

Create a Basic Pipeline in Azure Machine Learning Studio

In this hands-on lab, you work as a Machine Learning Operations Engineer for Avendador. During the lab, you will create and execute a machine learning pipeline using sample Microsoft data sources (**Restaurant Ratings** and **Restaurant Feature Data**).

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Path Info

Level

Intermediate

Duration

1h 30m

Published

Jun 03, 2022

Challenge

Preparing the Environment
1. Log into the Azure portal using the credentials provided on the lab page. Be sure to use an incognito or private browser window to ensure you're using the lab account rather than your own.
2. In the search bar at the top, enter and select Machine Learning studio, and then launch the studio, which will open in a separate tab.
3. Create a Standard_D2_V2 compute instance and start it.
4. Create a "classic" Azure Machine Learning pipeline in Azure Machine Learning studio by selecting "Designer" from the left menu. Choose the "Classic Prebuilt" tab and then click the plus-sign to "Create a new pipeline using classic, pre-built components."
Challenge

Ingest Data and Select Key Columns
1. Add the sample datasets, Restaurant Ratings and Restaurant Feature Data, to the pipeline canvas.
2. Select placeID and rating from the Restaurant Ratings data source.
3. Select placeID, alcohol, dress_code, price, and Rambience from the Restaurant Feature Data source.
Challenge

Transform Data Sources
1. Join the data sources using placeID as key.
2. Replace missing data in columns (placeID, rating, alcohol, dress_code, price, Rambience) with 0.
Challenge

Split Data into Training and Test Data
1. Split data using a 60/40 split.
  - 60% should go to a filter using Pearson correlation
  - 40% should be used as test
2. Create a Pearson correlation feature selection using rating as a target column (select columns to transform and apply transformation).
3. Create a Boosted Decision Tree Regression with the following settings:
  - Create trainer mode: SingleParameter
  - Maximum number of leaves per tree: 20
  - Minimum number of leaves per tree: 10
  - Learning rate: 0.2
  - Total number of trees constructed: 100
4. Create Train Model using rating as label column.
Challenge

Score and Evaluate
1. Create Score Model activity.
2. Create Evaluate Model activity.
3. Submit Model.
4. Evaluate Results.

Author

A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.