1. Monitoring AI Model Performance with NewRelic Entity Tags


    In a cloud environment, it's common to have several resources that work together to support an application. An AI model running in the cloud might be one component of a larger system that includes databases, compute instances, and other services. Monitoring these resources and the application’s performance is crucial for maintaining system reliability and performance.

    To monitor AI model performance with NewRelic, you can use NewRelic’s monitoring capabilities and enrich the collected data with tags that provide additional context about your AI model. Using tags enables you to filter and group your monitoring data in a more granular way. For example, you can tag your model with its version number, the type of model, what dataset it was trained on, or other metadata that is important to your organization.

    In this Pulumi program for Python, we will use the New Relic Pulumi provider to set up entity tags for an AI model. Entity tags in NewRelic help to organize and categorize your monitored entities, making it easier to manage and query the performance data. We won't be creating the AI model itself; instead, we'll be focusing on how to use Pulumi to interact with NewRelic for monitoring purposes.

    Here is a Pulumi Python program that demonstrates how to use the newrelic.EntityTags resource to tag a hypothetical AI model entity within NewRelic:

    import pulumi import pulumi_newrelic as newrelic # Assuming the AI model is already deployed and has a NewRelic entity GUID, # we can apply tags to this entity for better organization and filtering. # The GUID (Globally Unique Identifier) represents the ID of the NewRelic entity # which you would get from NewRelic UI or API after the entity is registered. ai_model_guid = "YOUR_AI_MODEL_ENTITY_GUID" # Define new tags for the AI model entity. # Replace "model-type", "model-version", etc. with actual keys and values relevant to your AI model. ai_model_tags = [ {"key": "model-type", "values": ["neural-network"]}, {"key": "model-version", "values": ["1.0.0"]}, {"key": "dataset", "values": ["dataset-v3"]}, # Add more tags as needed to provide additional context for monitoring and analytics. ] # Use the newrelic.EntityTags resource to apply tags to the AI model. # For documentation see: https://www.pulumi.com/registry/packages/newrelic/api-docs/entitytags/ model_tags_resource = newrelic.EntityTags("aiModelTags", guid=ai_model_guid, tags=ai_model_tags ) # Export the tags for reference. # This will output the applied tags when you run `pulumi up` making it easy to verify them. pulumi.export("ai_model_tags", model_tags_resource.tags)

    Make sure to replace YOUR_AI_MODEL_ENTITY_GUID with the actual GUID of your AI model entity from NewRelic. Your AI model should already be set up to send monitoring data to NewRelic, and you should have the NewRelic entity GUID at hand before you can tag it.

    After defining the entity GUID and the desired tags, we create an instance of the newrelic.EntityTags resource, passing the GUID and the tags list to it. Each tag is a dictionary with a key and a corresponding list of values. You can include as many tags as needed to provide context for your AI model.

    After applying the tags to your model entity, you can use NewRelic's Query Language (NRQL) to filter and analyze your monitoring data using these tags. For example, you could query all data related to the "neural-network" model type or to a specific model version.

    When you run pulumi up, this program will provision the tagging configurations in NewRelic, and you will see an output with the created tags. This can be a crucial step in setting up efficient monitoring for your cloud-based AI models.