1. Kubernetes-based Machine Learning Model Serving on Amazon EKS


    When setting up a Kubernetes-based Machine Learning (ML) model serving platform on Amazon EKS, you will be building an orchestrated environment where machine learning models are deployed as microservices. These microservices can manage the lifecycle of the ML models and serve predictions via a REST API, gRPC, or other protocols.

    To achieve this, you'll first need to set up an Amazon EKS cluster. Amazon EKS is a managed Kubernetes service that makes it easier to run Kubernetes on AWS without needing to install and operate your own Kubernetes control plane. Once the cluster is up and running, you would deploy your ML models as services within the cluster. These services can scale up and down as needed and provide a reliable endpoint for serving predictions.

    In your EKS cluster, you'd likely want to use tools like Kubernetes Deployments and Services to manage your ML models. Additionally, you can incorporate other tools like KubeFlow or Seldon Core if you want an even more robust ML ops pipeline.

    Below is a Pulumi Python program that sets up an Amazon EKS cluster and provisions the necessary infrastructure to serve Kubernetes-based ML models:

    import pulumi import pulumi_eks as eks # Create an EKS cluster with the default configuration. cluster = eks.Cluster('my-cluster') # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig) # pulumi_eks is a high-level package that wraps the complexity of creating an EKS cluster # This will output a kubeconfig file that can be used with `kubectl` to interact with the cluster

    Here we've created an EKS cluster and exported the kubeconfig. You can use the exported kubeconfig with kubectl or other Kubernetes tools to manage your ML services.

    Please note that this example does not cover the deployment of the actual ML models, which would typically involve:

    1. Containerizing your ML model.
    2. Pushing the container image to a registry (e.g., Amazon ECR).
    3. Writing Kubernetes deployment manifests for the ML model services.
    4. Applying the manifests to your EKS cluster to deploy the model.

    For a real-world deployment, these steps would involve considerations around security, network policies, persistent storage, and possibly integrating with AWS services such as SageMaker for full-fledged ML workflows.

    This Pulumi program is the first step, establishing the Kubernetes environment required to serve your models. The next steps would be more specific to your ML operations and would require additional Pulumi code or kubectl commands that define your ML workloads and their configuration.