Supported machine learning providers

watsonx.ai Runtime and many third-party machine learning providers are supported for machine learning model evaluations.

Use one of these supported machine learning providers to perform payload logging, feedback logging, and to measure performance accuracy, runtime bias detection, explainability, and auto-debias function as part of your model evaluation.

watsonx.ai Runtime
Azure ML Studio
Azure ML Service
AWS SageMaker
Custom (The custom machine learning framework must have equivalent functionality to watsonx.ai Runtime.)

Support for multiple machine learning engines

You can provision multiple machine learning engines when you configure machine learning model evalations or use the Python SDK.

Adding providers for machine learning model evaluations

From the Configure tab, click Add machine learning provider.
Select the provider you want to add.
Enter the required information, such as credentials, and click Save.

Changing or updating details for machine learning providers

Click the tile menu the tile menu icon icon and then click View & edit details.

Adding machine learning providers by using the Python SDK

You can add more than one machine learning engine for model evaluations by using the Python API wos_client.service_providers.add method.

IBM watsonx.ai Runtime

To add the IBM watsonx.ai Runtime machine learning engine, run the following command:

WML_CREDENTIALS = {
                   "url": "https://us-south.ml.cloud.ibm.com",
                   "apikey": IBM CLOUD_API_KEY
}
 
wos_client.service_providers.add(
        name=SERVICE_PROVIDER_NAME,
        description=SERVICE_PROVIDER_DESCRIPTION,
        service_type=ServiceTypes.WATSON_MACHINE_LEARNING,
        deployment_space_id = WML_SPACE_ID,
        operational_space_id = "production",
        credentials=WMLCredentialsCloud(
            apikey=CLOUD_API_KEY,      ## use `apikey=IAM_TOKEN` if using IAM_TOKEN to initiate client
            url=WML_CREDENTIALS["url"],
            instance_id=None
        ),
        background_mode=False
    ).result

Microsoft Azure ML Studio

To add the Azure ML Studio machine learning engine, run the following command:

AZURE_ENGINE_CREDENTIALS = {
    "client_id": "",
    "client_secret": "",
    "subscription_id": "",
    "tenant": ""
}
 
wos_client.service_providers.add(
        name=SERVICE_PROVIDER_NAME,
        description=SERVICE_PROVIDER_DESCRIPTION,
        service_type=ServiceTypes.AZURE_MACHINE_LEARNING,
        #deployment_space_id = WML_SPACE_ID,
        #operational_space_id = "production",
        credentials=AzureCredentials(
            subscription_id= AZURE_ENGINE_CREDENTIALS['subscription_id'],
            client_id = AZURE_ENGINE_CREDENTIALS['client_id'],
            client_secret= AZURE_ENGINE_CREDENTIALS['client_secret'],
            tenant = AZURE_ENGINE_CREDENTIALS['tenant']
        ),
        background_mode=False
    ).result

Amazon SageMaker

To add the AWS Sagemaker machine learning engine, run the following command:

SAGEMAKER_ENGINE_CREDENTIALS = {
                   'access_key_id':””,
                   'secret_access_key':””,
                   'region': '}
 
wos_client.service_providers.add(
        name="AWS",
        description="AWS Service Provider",
        service_type=ServiceTypes.AMAZON_SAGEMAKER,
        credentials=SageMakerCredentials(
            access_key_id=SAGEMAKER_ENGINE_CREDENTIALS['access_key_id'],
            secret_access_key=SAGEMAKER_ENGINE_CREDENTIALS['secret_access_key'],
            region=SAGEMAKER_ENGINE_CREDENTIALS['region']
        ),
        background_mode=False
    ).result

Microsoft Azure ML Service

To add the Azure ML Service machine learning engine, run the following command:

service_type = "azure_machine_learning_service"
added_service_provider_result = wos_client.service_providers.add(
        name=SERVICE_PROVIDER_NAME,
        description=SERVICE_PROVIDER_DESCRIPTION,
        service_type = service_type,
        credentials=AzureCredentials(
            subscription_id= AZURE_ENGINE_CREDENTIALS['subscription_id'],
            client_id = AZURE_ENGINE_CREDENTIALS['client_id'],
            client_secret= AZURE_ENGINE_CREDENTIALS['client_secret'],
            tenant = AZURE_ENGINE_CREDENTIALS['tenant']
        ),
        background_mode=False
    ).result

Producing a list of machine learning providers

To view a list of all the bindings, run the list method:

client.service_providers.list()

Table 1. Service bindings
uid	name	service_type	created
e88ms###-####-####-############	My Azure ML Service engine	azure_machine_learning	2019-04-04T09:50:33.189Z
e88sl###-####-####-############	My Azure ML Studio engine	azure_machine_learning	2019-04-04T09:50:33.186Z
e00sjl###-####-####-############	WML instance	watson_machine_learning	2019-03-04T09:50:33.338Z
e43kl###-####-####-############	My AWS SageMaker engine	sagemaker_machine_learning	2019-04-04T09:50:33.186Z

For information about specific machine learning engines, see the following topics:

For a coding example, see the Watson OpenScale sample notebooks.

Parent topic: Evaluating AI models with Watson OpenScale