1. User Authentication for ML Model APIs with Keycloak OpenID


    User authentication is a critical component for securing Machine Learning (ML) Model APIs. It ensures that only authorized users can access the models' endpoints. One popular way to implement this authentication is through OpenID Connect (OIDC), a secure identity layer that works on top of the OAuth 2.0 protocol. Keycloak is an open-source identity and access management solution that supports OIDC.

    To use Keycloak for user authentication with ML Model APIs, you typically need to:

    1. Create a Keycloak Realm: A Realm in Keycloak represents a logical grouping of users, clients (applications), and identity providers. Each realm can have its own users, credentials, roles, and groups.

    2. Define a Keycloak Client: This represents your ML Model API application in Keycloak. You'll define things like the client ID, base URL, and allowed redirect URIs here.

    3. Define Client Scopes and Roles: These allow for fine-grained access control to your API, determining what authenticated users can and cannot do.

    4. Implement the Keycloak Adapter in your API: Your ML Model API service would need to integrate a Keycloak adapter that would intercept API calls, validate access tokens, and ensure that the client is authorized to access the endpoint.

    Let's create a simple Pulumi program that sets up Keycloak authentication for an ML Model API. The program will use the keycloak provider to create a realm, a client, client scopes, and a client policy. The model API itself, the deployment logic, and other components are beyond the program's scope here; we focus on the setup needed on the Keycloak side.

    Below is the Pulumi program in Python that demonstrates this setup:

    import pulumi import pulumi_keycloak as keycloak # Create a new Keycloak Realm for your organization ml_model_realm = keycloak.Realm("ml-model-realm", realm="ml-models", enabled=True, # The .apply method is preferred over direct attribute access when concatenating or combining outputs. display_name=pulumi.Output.concat("ML Models - ", pulumi.get_project())) # Create a Keycloak Client for the ML Model API ml_model_api_client = keycloak.openid.Client("ml-model-api-client", realm_id=ml_model_realm.id, client_id="ml-model-api", enabled=True, client_authenticator_type="client-secret", redirect_uris=[ "https://my-ml-model-api.example.com/*" ], # try using the higher-order functions like `apply` when transforming outputs. base_url=ml_model_realm.display_name.apply(lambda name: f"https://{name.lower().replace(' ', '-')}.example.com"), admin_url="https://my-ml-model-api.example.com/admin", web_origins=["+"], # Use with caution, allows all web origins; specify exact origins for production standard_flow_enabled=True, implicit_flow_enabled=False, # Recommended to be disabled for machine-to-machine communication direct_access_grants_enabled=True) # Define client scopes for fine-grained permissions ml_model_api_scope = keycloak.openid.ClientScope("ml-model-api-scope", realm_id=ml_model_realm.id, name="ml-model-api-scope", description="Scope for accessing ML Model API") # Export realm and client information pulumi.export("keycloak_realm", ml_model_realm.realm) pulumi.export("ml_model_api_client_id", ml_model_api_client.client_id) pulumi.export("ml_model_api_scope_id", ml_model_api_scope.id)

    Explanation of the program:

    • We start by importing the necessary Pulumi and Keycloak modules.
    • We create a new Keycloak realm called ml-model-realm that will serve as the authentication domain for our ML Model API and users.
    • We then set up a Keycloak client ml-model-api-client which represents the ML Model API in the Keycloak realm. Here, we define the protocol it uses, its unique client_id, the URLs for redirection after authentication, and other access settings like scopes and flows.
    • We add a ClientScope called ml-model-api-scope, which defines the specific actions and access levels a client can have after being authenticated. You can define multiple scopes if needed for varied levels of access.
    • Lastly, the pulumi.export statements make the created Keycloak realm and client details available outside the Pulumi program. These details will be instrumental in configuring the actual ML Model API service to validate tokens against this Keycloak setup.

    You will need to integrate your API with Keycloak using one of Keycloak's adapters or libraries and configure it to require tokens issued by Keycloak for authentication. The client ID and scope ID that we are exporting will be necessary for configuring the authentication on your API's side.

    Please make sure to handle the Keycloak client secrets and other sensitive information securely, not shown in this example for simplicity.