pycaret.time_series.deploy_model#

pycaret.time_series.deploy_model(model, model_name: str, authentication: dict, platform: str = 'aws')[source]#

This function deploys the transformation pipeline and trained model on cloud.

Example

>>> from pycaret.datasets import get_data
>>> data = get_data('airline')
>>> from pycaret.time_series import *
>>> exp_name = setup(data = data, fh = 12)
>>> arima = create_model('arima')
>>> deploy_model(
        model = arima, model_name = 'arima-for-deployment',
        platform = 'aws', authentication = {'bucket' : 'S3-bucket-name'}
    )
Amazon Web Service (AWS) users:

To deploy a model on AWS S3 (‘aws’), environment variables must be set in your local environment. To configure AWS environment variables, type aws configure in the command line. Following information from the IAM portal of amazon console account is required:

  • AWS Access Key ID

  • AWS Secret Key Access

  • Default Region Name (can be seen under Global settings on your AWS console)

More info: https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-envvars.html

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.

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

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