1. Kubernetes Orchestrated ML Pipelines


    To create Kubernetes orchestrated Machine Learning (ML) pipelines, you can leverage various cloud providers that offer managed Kubernetes services and machine learning tools. Specifically, for this purpose, you can use:

    • Amazon Web Services (AWS) with Amazon Elastic Kubernetes Service (EKS) and SageMaker for building and orchestrating ML pipelines.
    • Google Cloud Platform (GCP) with Google Kubernetes Engine (GKE) and AI Platform Pipelines.
    • Azure with Azure Kubernetes Service (AKS) and Azure Machine Learning.

    Let's create an ML pipeline using AWS services as an example. For this, we will use Pulumi's AWS library to define infrastructure that includes:

    1. An EKS Cluster to run our Kubernetes pods.
    2. A SageMaker Pipeline to define our ML workflow.

    Below is a Pulumi Python program that provision an EKS cluster and create a SageMaker pipeline. Note that this example assumes you've prepared the necessary ML models, data, and definitions for SageMaker, which is a complex topic on its own.

    First, you should have Pulumi installed and configured for use with AWS. Remember to set up your AWS credentials beforehand.

    Now, let's write the Pulumi program:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster to deploy the ML models to. cluster = eks.Cluster("eks-cluster", instance_type="t2.medium", desired_capacity=2, min_size=1, max_size=3, create_oidc_provider=True) # Define the SageMaker pipeline. # Please note that you will need to define the proper JSON pipeline definition. # The JSON is quite complex and depends on your specific use case. For simplicity, # this example uses a placeholder for the pipeline definition (`pipeline_definition_placeholder`). sagemaker_role = aws.iam.Role("sagemaker-role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole" }] }""" ) sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker-policy-attachment", role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGEMAKER_FULL_ACCESS ) pipeline_definition = """{ "Placeholder": "You will need to replace this string with your actual JSON definition of the ML pipeline." }""" # Replace this with your actual JSON definition sagemaker_pipeline = aws.sagemaker.Pipeline("ml-pipeline", role_arn=sagemaker_role.arn, pipeline_name="my-ml-pipeline", pipeline_description="My machine learning pipeline", pipeline_definition=pipeline_definition, tags={"Environment": "Dev"} ) # Export the cluster kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig)

    In this example:

    • We created an EKS cluster, which is AWS's managed Kubernetes service. The cluster will provide a scalable and secure environment to orchestrate our ML workloads.
    • We defined an IAM role for SageMaker with the necessary permissions.
    • We attached an AWS managed policy for full SageMaker access to the role.
    • We used a placeholder for the SageMaker pipeline definition.

    Remember that the pipeline definition is a JSON object that defines the steps your ML pipeline will carry out, such as data preparation, model training, and deployment. This definition should be crafted according to the specific needs of your ML workflow. You need to replace the pipeline_definition placeholder with your actual pipeline JSON.

    Before running this program with Pulumi, you should prepare your SageMaker pipeline definition, which is specific to your use case and requires a deep understanding of your ML workflow.

    The above program is a starting point, and in a real-world implementation, you'd add more details about how to build, train, and deploy ML models within the Kubernetes environment. As ML workflows can be highly specific, consider consulting the AWS SageMaker documentation and Pulumi examples for detailed guidance on creating a pipeline that fits your requirements.