Getting Beyond the Basics with Azure Machine Learning

This repository provides the supporting code for my presentation entitled Beyond the Basics with Azure ML.

Generating Data

This data comes from the Chicago Parking Ticket database, courtesy of Daniel Hutmacher. I sampled 1,000,000 records from it and the file I used is available in CSV format.

Import this into Azure ML using the Dataset name ChicagoParkingTicketsFolder. Be sure to upload this as a uri_folder instead of an MLtable or uri_file!

Running the Code

Basic Notebook

Import the notebook in the Notebook folder into Azure Machine Learning. You will need to create a compute instance to run this.

ML Pipeline

In order to run the ML pipeline notebooks and jobs locally, you will need to have the following installed on your machine:

• Python (preferably the Anaconda distribution), with pip installed: conda install -c anaconda pip
• The Azure CLI
• The Azure ML Azure CLI extension: az extension add -n ml
• Pip packages: pip install azure-ai-ml, pip install azure-identity
• Visual Studio Code
• The Azure ML Visual Studio Code extension

Before you run the code, make sure your console has you logged into Azure via CLI:

az login

Then, create a folder called .azureml and a file named config.json. The file should look like the following structure:

{
    "subscription_id": "YOUR SUBSCRIPTION ID",
    "resource_group": "YOUR RESOURCE GROUP",
    "workspace_name": "YOUR WORKSPACE NAME"
}

Note that you must be logged into az cli with an account which has access to the subscription, resource group, and workspace.

From there, run the training code:

python deploy-train.py

You can see the job in action by going to Azure ML Studio and viewing the "Chicago_Parking_Tickets_Code-First" experiment. There will be a new "train_pipeline" job.

For scoring, run the following code:

python deploy-score.py

This will create a batch endpoint and deployment, upload data to a Datastore in Azure ML, create a job to generate predictions, and downloads the resulting predictions to a local file called predictions.csv.

IMPORTANT NOTE -- You must explicitly grant rights to the account running deploy-score.py against the Azure ML workspace. I granted Owner because I was running this personally, but it must be explicitly granted and not just have ownership as a side effect of subscription-level or resource group-level rights.

If you do not do this, you will likely get a strange BY_POLICY error message when running this script.