Challenge 1 – Run model locally & and import repo into Azure DevOps
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Introduction
The objective of challenge is to forecast the daily transactions using time series ARIMA modeling. It includes processing the transactions data from AdventureWorks database, analyzing it, creating and registering an ARIMA model, and finally deploying the model to an ACI instance. This entire lifecycle is done using Azure ML Python SDK.
Time series is a series of data points collected or indexed in time order at regular time points. It is a sequence taken at successive equally spaced time points. For example, weather data collected every hour or stock data collected every day or sensor data collected every minute. As a result, we see a trend and seasonality in time-series datasets. It is this temporal nature that makes them different from other conventional datasets and warrants a different type of modeling. We will cover that in this challenge as forecasting is one of the most common and prevalent tasks in Machine Learning.
Description
- Create and setup a new project in Azure DevOps.
- Import project files from the
Data_and_Code.zipfile your instructor provides. - Create new service connections in Project Settings for your Azure ML service and Azure Subscription using Azure Resource Manager service principal. This will enable you to connect to external and remote services to execute tasks in a pipeline.
- Import project files from the
- Clone into VS Code and run the projects files locally to understand the forecasting project and explore the different files available.
NOTE: This is the data science part. The focus of this hack is not on data science, but more on MLOps to help you understand how you can apply DevOps practices and principles to accelerate your ML projects and increase the efficiency, quality, and consistency of your ML workflows. - Install library requirements to setup your environment.
- Configure your Azure ML Workspace for the project.
- HINT: Add workspace details in
config.json. You can download it from portal too. - NOTE: Alternatively, you can configure your Azure ML Workspace by using Azure DevOps pipeline variables.
- HINT: Add workspace details in
- Now that you have environment setup, explore and run locally the python files in the folder
service/code/. What are these files trying to do? What should be the order of execution?
Success Criteria
- Understand the contents of the python files under
service/code/. - Count the number of CSV files extracted into
Data/folder LOCALLY using a Python script in VS Code or any popular IDE. - Creating an ARIMA model locally using VS Code.
- Forecasting project imported into Azure DevOps.