1. AI Workflow Observability with Prometheus on Kubernetes

    Python

    To achieve AI workflow observability with Prometheus on Kubernetes, one can use the aws.amp.Workspace resource from the AWS Pulumi provider to set up an Amazon Managed Service for Prometheus workspace. This service will provide scalable and secure monitoring for your Kubernetes workloads.

    Let's go through each step:

    1. Creating a Prometheus Workspace: Using aws.amp.Workspace to create a workspace for Prometheus metrics.
    2. Monitoring Kubernetes Clusters: After setting up a workspace, you'll query and monitor the metrics from your Kubernetes clusters. This step will assume that you have Prometheus configured in your Kubernetes cluster to scrape and collect metrics.
    3. Export Key Information: For accessing the monitoring service, it's critical to export any necessary identifiers, such as the Prometheus workspace ID.

    Below is a Pulumi program in Python that accomplishes this setup:

    import pulumi import pulumi_aws as aws # Create a Prometheus Workspace on AWS. # This will provide us with a managed Prometheus service that we can use to monitor our Kubernetes cluster. prometheus_workspace = aws.amp.Workspace("prometheusWorkspace") # The workspace ID is important for configuring Prometheus server and accessing the managed Prometheus instance. pulumi.export("prometheusWorkspaceId", prometheus_workspace.id) # To use this AWS Prometheus workspace, you'll need to configure your in-cluster Prometheus server # with the correct endpoint and credentials to send metrics to AWS AMP. # Additionally, you could setup Grafana or another visualization tool to query the metrics from # this workspace. You would typically set this up within your Kubernetes cluster using the relevant # Pulumi resources for Kubernetes deployments/services or via Helm charts.

    In this code:

    • We import the necessary Pulumi modules for AWS.
    • We instantiate a Prometheus workspace with aws.amp.Workspace which sets up a managed environment for handling Prometheus metrics.
    • Finally, we export the workspace ID which will be used later to configure in-cluster Prometheus and any other monitoring tools.

    After running this Pulumi program, you will have created a managed Prometheus workspace in AWS. To complete your observability setup:

    • You need to have a Prometheus server running in your Kubernetes cluster, which can be set up using the official Prometheus Helm chart or Kubernetes manifests.
    • You would configure Prometheus to send metrics to the created AWS AMP workspace. This involves updating Prometheus configuration files with the AWS endpoint and workspace ID.
    • To visualize and query the metrics, you can set up Grafana, which can integrate with Prometheus as a data source.

    With this workspace ready, you can also use additional Pulumi resources for provisioning alert manager definitions, rule group namespaces, and integrating with visualization tools like Grafana as your observability requirements grow.


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