Performance Tracking for AI Inference Services

Question

Pulumi · Accepted Answer

To track the performance of AI inference services, you would generally look at metrics such as throughput, accuracy, response time, and other relevant indicators depending on the specifics of the service you are using. These metrics help ensure that the inference service performs adequately, meets your application's latency requirements, and stays within your cost budget. Monitoring can also provide insights that lead to optimizing model performance and infrastructure costs.

For the purpose of monitoring these services, you can leverage the cloud provider's monitoring tools. For instance, Google Cloud offers Vertex AI for managing machine learning models, and you can track various metrics using Google Cloud's logging and metrics infrastructure. Similarly, Azure has capabilities for monitoring the inference endpoints in Azure Machine Learning Services.

Below is an example program using Pulumi for setting up performance tracking on both Google Cloud Platform (GCP) and Azure. The program covers two scenarios corresponding to each cloud provider:

1. In Google Cloud, you can create a custom logs-based metric from the logs generated by the AI Inference service.
2. In Azure, you could set up an inference endpoint in Azure Machine Learning Services and monitor it.

Here's a Pulumi program in Python that describes how to set up these resources:

```python
import pulumi
import pulumi_gcp as gcp
import pulumi_azure_native as azure_native
from pulumi_azure_native.machinelearningservices import InferenceEndpoint, Sku

# Google Cloud - Creating a Custom Log-based Metric for AI performance.
class GCPMonitoring(pulumi.ComponentResource):
    def __init__(self, name: str, project_id: str, filter: str, opts=None):
        super().__init__('custom:monitoring:GCPMonitoring', name, {}, opts)
        
        # Create a custom log-based tracking metric
        self.metric = gcp.logging.Metric(f"{name}-metric",
                                         project=project_id,
                                         metric_descriptor=gcp.logging.MetricDescriptorArgs(
                                             metric_kind="DELTA",
                                             value_type="INT64",
                                         ),
                                         filter=filter,
                                         opts=pulumi.ResourceOptions(parent=self))

# Azure - Monitoring an Inference Endpoint in Azure Machine Learning Services.
class AzureMonitoring(pulumi.ComponentResource):
    def __init__(self, name: str, resource_group_name: str, workspace_name: str, location: str, opts=None):
        super().__init__('custom:monitoring:AzureMonitoring', name, {}, opts)
        
        # Creating an Azure Machine Learning Inference Endpoint for monitoring
        self.inference_endpoint = InferenceEndpoint(f"{name}-endpoint",
                                                    resource_group_name=resource_group_name,
                                                    workspace_name=workspace_name,
                                                    location=location,
                                                    kind="ACI",  # You can choose between AKS and ACI
                                                    sku=Sku(name="Standard_F2s_v2"),
                                                    opts=pulumi.ResourceOptions(parent=self))

# Configuration for GCP
gcp_project_id = "your-gcp-project-id"
gcp_log_filter = 'resource.type="ai_platform"'  # This filter would need to be tailored to your use case

# Configuration for Azure
azure_resource_group_name = "your-azure-resource-group"
azure_workspace_name = "your-azure-workspace"
azure_location = "East US"  # Choose the appropriate Azure region

# Create monitoring instances for both GCP and Azure
gcp_monitoring = GCPMonitoring('gcp-ai-monitoring', project_id=gcp_project_id, filter=gcp_log_filter)
azure_monitoring = AzureMonitoring('azure-ai-monitoring', resource_group_name=azure_resource_group_name, workspace_name=azure_workspace_name, location=azure_location)

# Export the resource names
pulumi.export('gcp_log_based_metric', gcp_monitoring.metric.id)
pulumi.export('azure_inference_endpoint', azure_monitoring.inference_endpoint.id)
```

In this program, we define two classes, `GCPMonitoring` and `AzureMonitoring`, which encapsulate the resources needed for performance tracking in their respective clouds. You can create instances of these classes, passing in the required configuration such as project ID for GCP and resource group name for Azure.

For Google Cloud, the logs-based metric is created with a filter to target AI Platform logs. You would replace the filter with the specific log filter for your AI inference services.

For Azure, the program creates an inference endpoint within the Azure Machine Learning workspace. You can monitor this endpoint using Azure's native monitoring tools.

This Pulumi program automates the creation of monitoring resources, making it easy to keep an eye on your AI inference services' performance in the cloud. To use this program, replace the placeholders like `your-gcp-project-id` with your actual project information.

Don't forget to adjust the configurations, such as `gcp_log_filter`, and choose the specific Azure region you want to deploy your resources in. Also, ensure that your Pulumi CLI and cloud providers are properly configured before running this program.