What is deploy model function in regression model in pycaret

Recipe Objective - What is the deploy_model function in the regression model in pycaret?

PyCaret provides the deploy_model function in the regression module.

This module is provided by the PyCaret python library. PyCaret is an open-source, less-code machine learning python library. PyCaret automates machine learning workflow.  PyCaret library is wrapped around some machine learning libraries such as sklearn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.

deploy_model function with Example:-

PyCaret provides "pycaret.regression.deploy_model()" function. Deploy model function deploys the transformation pipeline and trained model on cloud.

from pycaret.datasets import get_data
bost = get_data('boston')
# importing regression module
from pycaret.regression import *
# initialize setup
set_up = setup(data = bost, target = 'medv')
# logistic regression
log_reg = create_model('lr')
# sets appropriate credentials for the platform as environment variables
import os
# set your cloud ACCESS_KEY_ID, SECRET_ACCESS_KEY
os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws',
    authentication = {'bucket' : 'S3-bucket-name'})