This is the companion code for the Santa's Helper blog. The blog explains what the code is trying to do, but focuses on the logic output - rather than the "how to". This code is the "how to".
In order to setup and run this code, you will need a Google Cloud account, with a credit card attached (this project requires some paid resources - although it won't cost much to run so long as you shut everything down once you've finished).
Next, create a new project in your GCP (Google Cloud Platform) account. The one used here and with the demonstration
has a project id of mv-santas-helper (if you let Google select a random project id, you can see what it is
by navigating to the Cloud Overview page).
There are a number of technologies used for this project, which are listed below along with their respective usages.
- BigQuery - Used as the underlying RDB (Relational Database). Sample child & present data is created using views. Additional views are then added as required in order to obtain the score for every child/parent combination.
- Vertex AI - Specifically, we are using a hosted/managed Jupyter Notebook to review the data in BigQuery and demonstrate the allocation logic.
There are few steps required to setup your environment to make all the code run. These are listed below - in the order they should be executed.
- Notebook Setup - Setting up the managed Jupyter Notebook in GCP
- Service Account Setup - Setting up the GCP Service Account - required by the Jupyter Notebook to create and access the data in BigQuery.
- BigQuery Setup - Initialising BigQuery for use by the Notebook - specifically creating a dataset.
- Running the Notebook - working through the algorithm and allocating presents.