1. AI Workflow Execution Tracing with Datadog


    To set up AI Workflow Execution Tracing with Datadog, you would typically integrate Datadog's monitoring and analytics capabilities with your workflow execution system. You can monitor your AI workflows through Datadog's custom metrics and dashboards, providing visibility into each step of the workflow, performance metrics, and potential errors.

    Here's a Pulumi Python program that sets up custom metrics in Datadog that could be used for AI Workflow Execution Tracing:

    import pulumi import pulumi_datadog as datadog # Here we're setting up a custom metric in Datadog. This metric could be used to trace specific steps # in an AI workflow, such as the duration of a training job or the number of processed data points. # You would send these metrics from your workflow execution system to Datadog using Datadog's API. # Create a Datadog metric metadata instance for AI workflow execution tracing ai_workflow_execution_metric = datadog.MetricMetadata("ai-workflow-execution-metric", metric="ai_workflow.execution_time", # Name of the metric # The metric type: can be gauge, rate, count, etc. ("gauge" is used for this example) type="gauge", # A brief description of the metric description="Tracks the time taken for each execution of the AI workflow", # Unit can be seconds, milliseconds, etc. It represents the measurement unit for the metric unit="second", # Optional: If the metric is a ratio, specify the per-unit (e.g. "request", "operation") per_unit="execution", # Optional: Give a short name that appears in the legend of the metric dashboard short_name="Execution Time", # Optional: The interval for sending the aggregated metrics to Datadog statsd_interval=10 ) # pulumi.export() is used to output the name of the metric once the program is run. # This way, you can see the generated name in the Pulumi service or CLI. pulumi.export("ai_workflow_metric_name", ai_workflow_execution_metric.metric)

    In the program above, we set up a Datadog metric that tracks the execution time of AI workflows. The datadog.MetricMetadata resource is used for this purpose, and it requires the metric parameter, which is the name for the custom metric we want to track. Other useful parameters include type, which defines the type of the metric (e.g., gauge for measuring values that can go up and down), description, and unit for specifying the measurement unit, such as seconds for time measurements.

    You would then use Datadog’s client libraries or the HTTP API to send the actual metric values from your AI workflow system to Datadog. Those values would then appear on your Datadog dashboard, where you can graph them, set up alerts, and so on, providing real-time tracing of your AI workflows.

    For more details on using the Datadog provider in Pulumi, you can refer to the Datadog provider documentation.

    Remember to replace the placeholders (like the metric name and type) with the actual values that correspond to the metrics you want to trace in your AI workflows.