Set Up Azure Cognitive Services
- Sign in to the [Azure portal](https://portal.azure.com) and create a new resource group for better management.
- Navigate to "Create a resource" > "AI + Machine Learning" and select the specific Cognitive Service you need (e.g., Text Analytics, Computer Vision).
- Fill in the required details such as subscription, resource group, service region, and pricing tier. Once done, click "Review + create" and then "Create" to initialize the service.
- Go to the resource overview page and copy the API key and endpoint URL for future use.
Install and Configure Prometheus
- Install Prometheus by downloading the latest binary from the [official site](https://prometheus.io/download/). Extract and save it in a desired location on your server.
- Edit the `prometheus.yml` file to configure Prometheus. Add scrape configurations to specify which services Prometheus should monitor.
- Launch Prometheus using the following command:
./prometheus --config.file=prometheus.yml
- Access the Prometheus web interface by navigating to `http://localhost:9090` in your web browser, where you can verify the integration status.
Set Up Azure Monitor for Prometheus
- Install the Azure Monitor exporter to expose metrics for Prometheus. You can use a pre-built Docker image or build it from the source available on [GitHub](https://github.com/Azure/azure-cloud-metrics-exporter).
- Run the Azure Monitor exporter with your Azure credentials securely stored, for example, in environment variables or a secured file.
- Modify `prometheus.yml` to scrape metrics served by the exporter:
scrape_configs:
- job_name: 'azure_monitor'
static_configs:
- targets: ['<exporter_host>:<exporter_port>']
- Restart Prometheus to apply the changes and start scraping metrics from Azure Monitor.
Integrate Azure Cognitive Services with Prometheus
- Create a service that communicates with Azure Cognitive Services using the SDK or REST API with the previously saved API key and endpoint.
- Deploy a custom exporter service that collects data from Azure Cognitive Services and formats it for Prometheus.
- Code Example: Fetch metrics from Azure and expose to Prometheus:
from prometheus_client import start_http_server, Gauge
from azure_ai_service import AzureServiceClient
azure_client = AzureServiceClient(endpoint='<your-endpoint>', api_key='<your-api-key>')
# Define a gauge for holding metrics
cog_service_usage = Gauge('cog_service_usage', 'Usage metrics for Azure Cognitive Services')
def fetch_and_export_metrics():
metrics = azure_client.get_metrics()
cog_service_usage.set(metrics['usage'])
if __name__ == '__main__':
start_http_server(8000) # Port where metrics are exposed
while True:
fetch_and_export_metrics()
- Update `prometheus.yml` to include the new endpoint running the custom export service:
scrape_configs:
- job_name: 'azure_cognitive'
static_configs:
- targets: ['localhost:8000']
- Reload or restart Prometheus, then check the Prometheus UI to ensure that the metrics from Azure Cognitive Services are being captured and displayed.
