This SageMaker example showcases how you can deploy a custom text classification model using Amazon Comprehend and SageMaker Pipelines.

sm_pipeline_with_comprehend.ipynb: Notebook explaining the pipeline step-by-step.

prepare_data.py: Script used in ComprehendProcess step in pipeline for data preparation used for training and testing.

train_eval_comprehend.py: Script used in ComprehendTrainAndEval step in pipeline to train and evaluate the Amazon Comprehend model.

deploy_comprehend.py: Script used in ComprehendDeploy step in pipeline to deploy an Amazon Comprehend model endpoint.

iam_helper.py: Helper function to create and delete an IAM role for the Lambda function used in LambdaStep.

test_comprehend_lambda.py: Lambda handler used to perform inference using the Amazon Comprehend model endpoint.