[spike] Enable IA to decision on provision or extend mac machines

Get data information

• Record each request (team, time of execution, UTC datetime)
• Record costs
• Record unattended requests / Waiting time

Feet an IA model:

• Linear or Polynomial Regression

This type of model could be useful if you're looking to predict a continuous variable, such as the number of machines needed or the duration of requests. You can adjust the model to minimize costs or maximize performance.

• Time Series Models:

If your data has a strong temporal component, such as daily or seasonal patterns, you might consider time series models like ARIMA (Autoregressive Integrated Moving Average) or more advanced models like LSTM (Long Short-Term Memory).

• Logistic Regression or Classification:

If you are more interested in making binary decisions, such as whether to provision a new machine at a given time, you could use logistic regression for binary classification problems.

Deploy the model and handle the shared pool based on it.

The issue names no files, tests, or entry points; start by clarifying the data sources, model choice, provisioning integration, and acceptance criteria. Done should include recording request timing, costs, unattended requests, and waiting time, then deploying a model that informs shared mac machine provisioning or extension decisions.