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

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!

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

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.