Monitor Sagemaker ML With Watson OpenScale

In this Code Pattern, we will use a German Credit dataset to create a logistic regression model using AWS SageMaker. We will use Watson OpenScale to bind the ML model deployed in the AWS cloud, create a subscription, and perform payload and feedback logging.

When the reader has completed this Code Pattern, they will understand how to:

• Prepare data, train a model, and deploy using AWS SageMaker
• Score the model using sample scoring records and the scoring endpoint
• Bind the SageMaker model to the Watson OpenScale Data Mart
• Add subscriptions to the Data Mart
• Enable payload logging and performance monitoring for both subscribed assets
• Use Data Mart to access tables data via subscription

Flow

1. The developer creates a Jupyter Notebook.
2. The Jupyter Notebook is connected to a PostgreSQL database, which is used to store Watson OpenScale data.
3. An ML model is created using Amazon SageMaker, using data from credit_risk_training.csv, and then it is deployed to the cloud.
4. Watson Open Scale is used by the notebook to log payload and monitor performance.

Prerequisites

• An IBM Cloud Account
• IBM Cloud CLI
• IBM Cloud Object Storage (COS)
• An account on AWS SageMaker

Steps

1. Clone the repository
2. Create a Watson OpenScale service
3. Run the notebooks

1. Clone the repository

git clone https://github.com/IBM/monitor-sagemaker-ml-with-watson-openscale
cd monitor-sagemaker-ml-with-watson-openscale

2. Create a Watson OpenScale service

Create Watson OpenScale, either on the IBM Cloud or using your On-Premise Cloud Pak for Data.

On IBM Cloud

• If you do not have an IBM Cloud account, register for an account

• Create a Watson OpenScale instance from the IBM Cloud catalog

• Select the Lite (Free) plan, enter a Service name, and click Create.

• Click Launch Application to start Watson OpenScale.

• Click Auto setup to automatically set up your Watson OpenScale instance with sample data.

  

• Click Start tour to tour the Watson OpenScale dashboard.

On IBM Cloud Pak for Data platform

Note: This assumes that your Cloud Pak for Data Cluster Admin has already installed and provisioned OpenScale on the cluster.

• In the Cloud Pak for Data instance, go the (☰) menu and under Services section, click on the Instances menu option.

  

• Find the OpenScale-default instance from the instances table and click the three vertical dots to open the action menu, then click on the Open option.

  

• If you need to give other users access to the OpenScale instance, go the (☰) menu and under Services section, click on the Instances menu option.

  

• Find the OpenScale-default instance from the instances table and click the three vertical dots to open the action menu, then click on the Manage access option.

  

• To add users to the service instance, click the Add users button.

  

• For all of the user accounts, select the Editor role for each user and then click the Add button.

  

4. Run the notebooks

There are 2 notebooks for this pattern.

Run CreditModelSagemakerLinearLearner.ipynb

Begin by running CreditModelSagemakerLinearLearner.ipynb

• You will need to setup AWS SageMaker

Get AWS keys

• Get your AWS security credentials from the AWS console by highlighting your logged-in user and going to My Security Credentials:

• Click Access keys (access key ID and secret access key) -> Create Access Key and then download the key (and/or copy it from the screen) to obtain both the AWSAccessKeyId and the AWSSecretKey:

• Insert your AWS credentials after the cell 2.4.1 Add AWS credentials. You can find your region_name from the URL of the AWS console when you are logged in, i.e. my URL is https://us-east-2.console.aws.amazon.com/console/home?region=us-east-2 so my region_name is us-east-2.

• Get your bucket_name by running 2.4.2 Get bucket name and using the default bucket, or run the cell that follows using [bkt.name for bkt in s3.buckets.all()] to choose another bucket.

• Insert your AWS S3 bucket_name after the cell 2.4 Create an S3 bucket and use the name in the cell below for bucket_name

• Move your cursor to each code cell and run the code in it. Read the comments for each cell to understand what the code is doing. Important when the code in a cell is still running, the label to the left changes to In [*]:. Do not continue to the next cell until the code is finished running.

Run AIOpenscaleSagemakerMLengine.ipynb

Next Run AIOpenscaleSagemakerMLengine.ipynb

• Follow the instructions for ACTION: Get Watson OpenScale instance_guid and apikey using the IBM Cloud CLI

How to get api key using ibmcloud console:

ibmcloud login --sso
ibmcloud iam api-key-create 'my_key'

• Enter the CLOUD_API_KEY you just created after the cell 1.1.1 Add the IBM Cloud apikey as CLOUD_API_KEY

Create COS bucket and get credentials

• In your IBM Cloud Object Storage instance, create a bucket with a globally unique name. The UI will let you know if there is a naming conflict. This will be used in cell 1.3.1 as BUCKET_NAME.

• In your IBM Cloud Object Storage instance, get the Service Credentials for use as COS_API_KEY_ID, COS_RESOURCE_CRN, and COS_ENDPOINT:

  

• Add the COS credentials in cell 1.3

• Insert your BUCKET_NAME in the cell 1.3.1 Add the BUCKET_NAME.

• In the cell after 2.1 Bind SageMaker machine learning engine enter your SAGEMAKER_ENGINE_CREDENTIALS using the keys you obtained in the step above for Get AWS keys.

• After running 3.1 Add subscriptions you will get a source_uid to enter in the cell that follows.

• Move your cursor to each code cell and run the code in it. Read the comments for each cell to understand what the code is doing. Important when the code in a cell is still running, the label to the left changes to In [*]:. Do not continue to the next cell until the code is finished running.

Sample Output

See the example output for CreditModelSagemakerLinearLearner-with-outputs.ipynb See the example output for AIOpenscaleSagemakerMLengine-with-output.ipynb

License

This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 and the Apache License, Version 2.

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