AWS SageMaker Deployment for Batch Transform

I am trying to use a XGBoost model in Sage Maker and use it to score for a large data stored in S3 using Batch Transform.

I build the model using existing Sagemaker Container as follows:

estimator = sagemaker.estimator.Estimator(image_name=container, 
                                          hyperparameters=hyperparameters,
                                          role=sagemaker.get_execution_role(),
                                          train_instance_count=1, 
                                          train_instance_type='ml.m5.2xlarge', 
                                          train_volume_size=5, # 5 GB 
                                          output_path=output_path,
                                          train_use_spot_instances=True,
                                          train_max_run=300,
                                          train_max_wait=600)

estimator.fit({'train': s3_input_train,'validation': s3_input_test})

The following code is used to do Batch Transform

 The location of the test dataset
batch_input = 's3://{}/{}/test/examples'.format(bucket, prefix)

# The location to store the results of the batch transform job
batch_output = 's3://{}/{}/batch-inference'.format(bucket, prefix)

transformer = xgb_model.transformer(instance_count=1, instance_type='ml.m4.xlarge', output_path=batch_output)

transformer.transform(data=batch_input, data_type='S3Prefix', content_type='text/csv', split_type='Line')

transformer.wait()

The above code works fine in Development environment (Jupyter notebook) when the model is built in Jupyter. However, I would like to deploy the model and call its endpoint for Batch Transform.

Most examples for SageMaker endpoint creation is for scoring on a single data and not for batch transform.

Can someone point to how to deploy and use the endpoints for Batch Transform in SageMaker? Thank you