1. Real-Time AI Model Rollouts with Knative Revision Management


    Real-time AI model rollouts are a sophisticated use-case involving the deployment and management of machine learning models in a production environment. It usually requires dynamic scaling, versioning, traffic routing, and rollback capabilities to ensure that new models are rolled out smoothly and with minimal impact on live traffic.

    Knative is a Kubernetes-based platform to deploy and manage modern serverless workloads. It takes care of the underlying infrastructure plumbing and provides features such as autoscaling (including scale-to-zero), revision tracking, traffic routing for staged rollouts, and out-of-the-box support for various patterns like canary or blue-green deployments.

    In a Pulumi program, you can leverage Kubernetes resources and custom controllers to handle these kinds of operations. However, the rollout of machine-learning models in real-time often involves custom logic and integration with ML pipelines, making it a very domain-specific scenario.

    The provided Pulumi Registry Results suggest some Kubernetes-related resources, but there's no direct mention of Knative resources which would be required for Knative revision management. However, Pulumi's Kubernetes provider can be used to define and manage Knative resources, as they are ultimately just custom Kubernetes resources.

    Below is an example of how you could use Pulumi to deploy a Knative Service which internally manages revisions. As real-time AI model rollouts can be very specific, we'll focus on the infrastructure part, assuming that you have the model packaged in a container that can be deployed by Knative.

    Please note that to run this program, you'd need to have Pulumi set up along with kubectl configured to point to a Kubernetes cluster where Knative has been installed. Your AI model image should be hosted on a container registry accessible by your Kubernetes cluster.

    import pulumi import pulumi_kubernetes as k8s # The name of the namespace where Knative is installed and where our service will be deployed. knative_namespace = "knative-serving" # The container image URL for your AI model. This should point to the image that contains your AI model. model_image_url = "gcr.io/my-project/my-model:v1" # A Knative Service manages the entire lifecycle of your workload. # It creates the necessary networking routes, revisions, and autoscaling configurations for your container. model_service = k8s.apiextensions.CustomResource( "ai-model-service", api_version="serving.knative.dev/v1", kind="Service", metadata={ "name": "ai-model-service", "namespace": knative_namespace, }, spec={ "template": { "spec": { "containers": [ { "image": model_image_url, # Define resources requests and limits so Knative can manage scaling of the pod automatically. "resources": { "requests": { "cpu": "100m", "memory": "256Mi", }, "limits": { "cpu": "200m", "memory": "512Mi", }, }, } ] } }, # Define the traffic routing settings, in this case, 100% traffic to the latest revision. "traffic": [ { "latestRevision": True, "percent": 100, } ], }, opts=pulumi.ResourceOptions(provider=k8s_provider), # Assumes an existing Pulumi provider for Kubernetes is configured. ) # Exporting the status of the Knative Service, including URL to access the service once it's deployed. pulumi.export("model_service_status", model_service.status)

    In this example:

    • knative_namespace: The Kubernetes namespace where Knative is installed and where the service will be deployed.
    • model_image_url: The URL of the container image which includes your AI model.
    • pulumi_kubernetes.apiextensions.CustomResource: This will create a custom Kubernetes resource — in this case, a Knative Service that Knative understands.
    • model_service.spec.template: Contains the description of the deployment. It points to the container image of your AI model and optionally configures resource requests and limits.
    • model_service.spec.traffic: Manage the distribution of traffic between different revisions of your service.

    The Knative service will create the necessary Kubernetes resources to manage your AI model's deployment. It will ensure that the model is accessible over the network and automatically creates revisions whenever you update your model's container image URL.

    This is a very basic example. Depending on your actual use case and model serving requirements, you might have to incorporate other features such as various authentication and authorization mechanisms, integrating with data pipelines, and possibly using a service mesh for sophisticated traffic management.