How to avoid reloading ML model every time when I call python script?

There are a few ways you can avoid reloading your machine learning (ML) model every time you call your Python script:

1. Load the model once and save it to a global variable: If you are using the same model throughout your script, you can load the model once at the beginning of your script and save it to a global variable. You can then use this global variable to make predictions without having to reload the model each time.
2. Use a persistent model: If you are using a model that can be persisted (saved to disk and loaded later), you can save the model to disk after you have trained it and then load it each time you need to make a prediction. This can be more efficient than training the model from scratch each time.
3. Use a server to host the model: If you are making many predictions and don't want to load the model each time, you can set up a server to host the model and make predictions using an API. This way, you can make predictions by sending data to the server and receiving the prediction in return, without having to load the model on the client side.
4. Use a cache: If you are making the same prediction multiple times, you can use a cache to store the results of the prediction and reuse them without having to re-run the model. This can be especially useful if your model is computationally expensive to run.