docstr: This function deploys the transformation pipeline and trained model on cloud.
  
  
  Example
  -------
  >>> from pycaret.datasets import get_data
  >>> boston = get_data('boston')
  >>> from pycaret.regression import *
  >>> exp_name = setup(data = boston,  target = 'medv')
  >>> lr = create_model('lr')
  >>> # sets appropriate credentials for the platform as environment variables
  >>> import os
  >>> 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'})
  
  
  Amazon Web Service (AWS) users:
      To deploy a model on AWS S3 ('aws'), the credentials have to be passed. The easiest way is to use environment
      variables in your local environment. Following information from the IAM portal of amazon console account
      are required:
  
      - AWS Access Key ID
      - AWS Secret Key Access
  
      More info: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables
  
  
  Google Cloud Platform (GCP) users:
      To deploy a model on Google Cloud Platform ('gcp'), project must be created
      using command line or GCP console. Once project is created, you must create
      a service account and download the service account key as a JSON file to set
      environment variables in your local environment.
  
      More info: https://cloud.google.com/docs/authentication/production
  
  
  Microsoft Azure (Azure) users:
      To deploy a model on Microsoft Azure ('azure'), environment variables for connection
      string must be set in your local environment. Go to settings of storage account on
      Azure portal to access the connection string required.
  
      - AZURE_STORAGE_CONNECTION_STRING (required as environment variable)
  
      More info: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?toc=%2Fpython%2Fazure%2FTOC.json
  
  
  model: scikit-learn compatible object
      Trained model object
  
  
  model_name: str
      Name of model.
  
  
  authentication: dict
      Dictionary of applicable authentication tokens.
  
      When platform = 'aws':
      {'bucket' : 'S3-bucket-name', 'path': (optional) folder name under the bucket}
  
      When platform = 'gcp':
      {'project': 'gcp-project-name', 'bucket' : 'gcp-bucket-name'}
  
      When platform = 'azure':
      {'container': 'azure-container-name'}
  
  
  platform: str, default = 'aws'
      Name of the platform. Currently supported platforms: 'aws', 'gcp' and 'azure'.
  
  
  Returns:
      None
docstr_tok: ["This","function","deploys","the","transformation","pipeline","and","trained","model","on","cloud",".","Example","--","--","--","-",">",">",">","from","pycaret.datasets","import","get_data",">",">",">","boston","=","get_data","(","'boston","'",")",">",">",">","from","pycaret.regression","import","*",">",">",">","exp_name","=","setup","(","data","=","boston",",","target","=","'medv","'",")",">",">",">","lr","=","create_model","(","'lr","'",")",">",">",">","#","sets","appropriate","credentials","for","the","platform","as","environment","variables",">",">",">","import","os",">",">",">","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","'","}",")","Amazon","Web","Service","(","AWS",")","users",":","To","deploy","a","model","on","AWS","S3","(","'aws","'",")",",","the","credentials","have","to","be","passed",".","The","easiest","way","is","to","use","environment","variables","in","your","local","environment",".","Following","information","from","the","IAM","portal","of","amazon","console","account","are","required",":","-","AWS","Access","Key","ID","-","AWS","Secret","Key","Access","More","info",":","https",":","\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/guide\/credentials.html","#","environment-variables","Google","Cloud","Platform","(","GCP",")","users",":","To","deploy","a","model","on","Google","Cloud","Platform","(","'gcp","'",")",",","project","must","be","created","using","command","line","or","GCP","console",".","Once","project","is","created",",","you","must","create","a","service","account","and","download","the","service","account","key","as","a","JSON","file","to","set","environment","variables","in","your","local","environment",".","More","info",":","https",":","\/\/cloud.google.com\/docs\/authentication\/production","Microsoft","Azure","(","Azure",")","users",":","To","deploy","a","model","on","Microsoft","Azure","(","'azure","'",")",",","environment","variables","for","connection","string","must","be","set","in","your","local","environment",".","Go","to","settings","of","storage","account","on","Azure","portal","to","access","the","connection","string","required",".","-","AZURE_STORAGE_CONNECTION_STRING","(","required","as","environment","variable",")","More","info",":","https",":","\/\/docs.microsoft.com\/en-us\/azure\/storage\/blobs\/storage-quickstart-blobs-python","?","toc=","%","2Fpython","%","2Fazure","%","2FTOC.json","model",":","scikit-learn","compatible","object","Trained","model","object","model_name",":","str","Name","of","model",".","authentication",":","dict","Dictionary","of","applicable","authentication","tokens",".","When","platform","=","'aws","'",":","{","'bucket","'",":","'S3-bucket-name","'",",","'path","'",":","(","optional",")","folder","name","under","the","bucket","}","When","platform","=","'gcp","'",":","{","'project","'",":","'gcp-project-name","'",",","'bucket","'",":","'gcp-bucket-name","'","}","When","platform","=","'azure","'",":","{","'container","'",":","'azure-container-name","'","}","platform",":","str",",","default","=","'aws'","Name","of","the","platform",".","Currently","supported","platforms",":","'aws","'",",","'gcp","'","and","'azure","'",".","Returns",":","None"]
code: def deploy_model(
      self,
      model,
      model_name: str,
      authentication: dict,
      platform: str = "aws",
  ):
      """
      This function deploys the transformation pipeline and trained model on cloud.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> # sets appropriate credentials for the platform as environment variables
      >>> import os
      >>> 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'})
  
  
      Amazon Web Service (AWS) users:
          To deploy a model on AWS S3 ('aws'), the credentials have to be passed. The easiest way is to use environment
          variables in your local environment. Following information from the IAM portal of amazon console account
          are required:
  
          - AWS Access Key ID
          - AWS Secret Key Access
  
          More info: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables
  
  
      Google Cloud Platform (GCP) users:
          To deploy a model on Google Cloud Platform ('gcp'), project must be created
          using command line or GCP console. Once project is created, you must create
          a service account and download the service account key as a JSON file to set
          environment variables in your local environment.
  
          More info: https://cloud.google.com/docs/authentication/production
  
  
      Microsoft Azure (Azure) users:
          To deploy a model on Microsoft Azure ('azure'), environment variables for connection
          string must be set in your local environment. Go to settings of storage account on
          Azure portal to access the connection string required.
  
          - AZURE_STORAGE_CONNECTION_STRING (required as environment variable)
  
          More info: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?toc=%2Fpython%2Fazure%2FTOC.json
  
  
      model: scikit-learn compatible object
          Trained model object
  
  
      model_name: str
          Name of model.
  
  
      authentication: dict
          Dictionary of applicable authentication tokens.
  
          When platform = 'aws':
          {'bucket' : 'S3-bucket-name', 'path': (optional) folder name under the bucket}
  
          When platform = 'gcp':
          {'project': 'gcp-project-name', 'bucket' : 'gcp-bucket-name'}
  
          When platform = 'azure':
          {'container': 'azure-container-name'}
  
  
      platform: str, default = 'aws'
          Name of the platform. Currently supported platforms: 'aws', 'gcp' and 'azure'.
  
  
      Returns:
          None
  
      """
  
      return super().deploy_model(
          model=model,
          model_name=model_name,
          authentication=authentication,
          platform=platform,
      )
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 17 for calling the RegressionExperiment obj's underlying member method code with example usage: obj.deploy_model(model, model_name, authentication, platform) and returns: super