03-UnitTesting.md

Challenge 3 – Create a Unit Test Locally and in Azure DevOps

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Introduction

Use this challenge to perform tasks to confirm data has been extracted from source and saved in the working directory

Description

1. Make sure you have created a new project in Azure DevOps, created new service connections and have Azure ML workspace configured for the project using config.json file.

2. Write a Python snippet to validate that AdventureWorks data is indeed downloaded and extracted into Data/ folder. Do a preview of file count in the data folder. Additionally, you could also pick a csv file visualize the data.

   • HINT: For Azure DevOps, it is encouraged to leverage the Python Script task using the pipeline task manager in Azure DevOps

Success criteria

1. Count the number of CSV files extracted into Data/ folder using a Python script in Azure DevOps pipeline. (This can be the same script used in Challenge#1)

2. Visualizing and exploring the data (using a python visualization library like matplotlib or seaborn or plotly)

Learning resources

• MLOps Home page to discover more
• MLOps documentation: Model management, deployment, and monitoring with Azure Machine Learning
• A blog on MLOps - How to accelerate DevOps with ML Lifecycle Management
• MLOps Reference Architecture