1. Real-time Dashboard for ML Model Inference Metrics with Grafana


    To create a real-time dashboard for ML model inference metrics using Grafana, you would need to set up a Grafana server, which could be self-managed on an existing cloud infrastructure, or provisioned as a service. For demonstration purposes, let's assume you choose to provision a Grafana server on Aiven, a DBaaS that offers a managed Grafana service.

    Aiven simplifies the management of Grafana and allows you to focus on dashboard design and metrics analysis rather than infrastructure management. The Pulumi resources related to Grafana via the Aiven provider enable you to declare and manage the Grafana resource in code.

    Below I will show you how to use Pulumi with the Aiven provider to provision a Grafana server. This code demonstrates setting up the Grafana service with the required configurations. For demonstration purposes, let's assume that you already have data sources and metrics that you want to track with Grafana, although setting up data sources and creating the dashboard itself happens within Grafana and is beyond the scope of the infrastructure code.


    1. aiven.Grafana: This is a Pulumi resource defined by the Aiven provider that allows you to create a managed Grafana service.
      • plan: Specifies the service plan size, which affects the resources available to your Grafana service.
      • project: The Aiven project to which the Grafana service belongs.
      • serviceName: The name of the Grafana service.
      • grafanaUserConfig: Configuration parameters for the Grafana service. You can enable features like public access, authentication methods, and more.

    Now, let's proceed with the Pulumi Python program code that provisions a Grafana instance on Aiven:

    import pulumi import pulumi_aiven as aiven # Create an Aiven Grafana instance grafana_service = aiven.Grafana("my-grafana-service", plan="business-4", # The plan determines the size and capacity of the service. project="my-aiven-project", # Replace this with your Aiven project name. serviceName="ml-dashboard-grafana", # The name of your Grafana service. grafanaUserConfig=aiven.GrafanaGrafanaUserConfigArgs( publicAccess=aiven.GrafanaGrafanaUserConfigPublicAccessArgs( grafana=True # Make Grafana accessible publicly for ease of access. ), # Authentication, alerting, and other configurations can be specified here auth_google=aiven.GrafanaGrafanaUserConfigAuthGoogleArgs( clientId="your-google-client-id", clientSecret="your-google-client-secret", allowSignUp=True, allowedDomains=["yourdomain.com"], # Restrict authentication to specific Google domains ), # Configure emails for Grafana alerts smtpServer=aiven.GrafanaGrafanaUserConfigSmtpServerArgs( host="smtp.yourdomain.com", port=587, username="your-smtp-username", password="your-smtp-password", fromAddress="grafana@yourdomain.com", ), ) ) # Export the Grafana service URL for easy access after deployment pulumi.export("grafana_service_url", grafana_service.service_uri)

    Make sure to replace the placeholders including my-aiven-project, your-google-client-id, your-google-client-secret, yourdomain.com, etc., with your actual project and domain details.

    After running pulumi up, this program will provision a new Grafana instance within your Aiven project. When it's finished, you can visit the service URL provided by the output to access your Grafana dashboard and start configuring it with data sources and dashboards related to your ML model's inference metrics.

    Remember, this Pulumi program sets up the infrastructure for Grafana. To create real-time dashboards or visualization panels that display your ML model inference metrics, you'll need to use Grafana's UI to configure data sources (e.g., Prometheus, InfluxDB) and then design the dashboard manually or import existing JSON dashboard configurations.