Model Exportation

To deploy the transformation pipeline and trained model on cloud.

Choose the platform where you want to export your model:

Amazon Web Service (AWS)

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

Enter S3 bucket name, the name of your model without .pkl extention, and the name that will be used when exporting to the platform without extention (.pkl). and click Export button.

Google Cloud Platform (GCP)

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

Enter gcp project name and gcp bucket name , the name of your model without .pkl extention, and the name that will be used when exporting to the platform without extention (.pkl).click Export button.

Microsoft Azure (Azure)

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

Enter azure container name, the name of your model without .pkl extention, and the name that will be used when exporting to the platform without extention (.pkl). and click Export button.