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.
Get data information
Feet an IA model:
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.
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).
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.