Sagemaker to RHOAI Fraud Detection

This tutorial is based on: https://rh-aiservices-bu.github.io/fraud-detection/ but instead of doing all the tasks in Red Hat OpenShift AI it tries to mmimic the scnerio where a Data Scientists develops a model in an AI Studio (like the ones AWS, IBM, Google or Azure offer) and then deploys it in Red Hat OpenShift AI.

There are some differences though:

• For now this repo starts in Sagemaker but the scenario is generic
• It uses Tekton to build the Kubeflow Pipeline (KFP) used to test and deploy the model
• There's a Camel integration that helps moving the model from Amazon Web Services S3 to an on-premise Red Hat OpenShift AI installation
• It uses Gitops to deploy all the components

Picture of the arquitecture and flow.

The simplified flow of the demonstration is as follows:

1. Go to Sagemaker Studio Lab. Train and save the model, scaler and test data as a zip file (evaluation kit) in an S3 bucket (start).
2. A Camel S3 integration will be triggered every time there's a new evaluation kit.

Preparation

Adjust minimal .env

Here you are sample .env file that should work straight away. Change REPO_URL if you have forked this repo and INSTANCE_NAME at will or if you want to deploy multiple times in the same cluster.

INSTANCE_NAME="fraud-detection"
cat <<EOF > ./bootstrap/.env
REPO_URL="https://github.com/alpha-hack-program/ai-studio-rhoai.git"
INSTANCE_NAME="${INSTANCE_NAME}"
DATA_SCIENCE_PROJECT_NAMESPACE="${INSTANCE_NAME}-ds"
EOF

AWS S3 access

Yo have to:

• Create a bucket you will use the bucket name later.
• Create ACCESS KEYS and give them all persmissions on the S3 bucket
• Save the AWS access key, secret and region in ./bootstrap/.creds file.

Adjust and run the next commands to create ./bootstrap/.creds file.

NOTE: Don't forget to log in using oc before running this command. AWS and Minio credentials should be updated!

. ./bootstrap/.env

MINIO_ENDPOINT=$(oc get route minio-s3 -n ic-shared-minio -o jsonpath='{.spec.host}')

cat <<EOF > ./bootstrap/.creds
AWS_ACCESS_KEY_ID="AK..."
AWS_SECRET_ACCESS_KEY="7S.."
AWS_REGION="eu-central-1"
AWS_S3_BUCKET="sagemaker-models-XYZ"
MINIO_ACCESS_KEY=minio
MINIO_SECRET_KEY=minio123
MINIO_REGION=none
MINIO_ENDPOINT=https://${MINIO_ENDPOINT}
MINIO_BUCKET=staging
EVALUATION_KIT_FILENAME=models/evaluation_kit.zip
KFP_PIPELINE_NAMESPACE=${DATA_SCIENCE_PROJECT_NAMESPACE}
KFP_PIPELINE_DISPLAY_NAME=deploy
EOF

Log in Sagemaker Studio

Log in or create an account in https://studiolab.sagemaker.aws.

Then clone this repository.

Open a terminal and run this command.

NOTE: Don't forget to log in using oc before running this command. AWS and Minio credentials should be updated!

cat <<EOF > .env
AWS_ACCESS_KEY_ID="AK..."
AWS_SECRET_ACCESS_KEY="7S.."
AWS_REGION="eu-central-1"
AWS_S3_BUCKET="sagemaker-models-XYZ"
EOF

Now open 1_experiment_train.ipynb and run all the cells. This notebook creates a NN model and trains it with the data in file ./csv/card_transdata.csv. The model is written to models/fraud/1/model.onnx. It also saves the scaler and evaluation data for later testing the model in artifact folder.

it's time to compress the evaluation kit that is: the model, scaler and evaluation data into models/evaluation_kit.zip. Finally the files are saved to the bucket you created in AWS S3.

Time to deploy our smart rest service for fraud detection in RHOAI

We're going to use ArgoCD to do it. It should be pretty straight forward if:

TODO: Explain how to do this

• Red Hat OpenShift Gitops is installed
• Red Hat OpenShift AI is installed and configured
• All the preparation steps have been executed

To deploy the application run these command:

NOTE: Yo have to be logged in using oc and have permissions to create Application object in openshift-gitops namespace.

cd bootstrap
./deploy.sh

And also this command that creates a secret called camel-s3-integration-creds in ${DATA_SCIENCE_PROJECT_NAMESPACE}:

NOTE: Don't forget that DATA_SCIENCE_PROJECT_NAMESPACE should be set in ./bootstrap/.env.

./setup-creds.sh

Create the staging bucket!!!