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|  How to Integrate Google Cloud AI with New Relic

How to Integrate Google Cloud AI with New Relic

January 24, 2025

Discover step-by-step integration of Google Cloud AI with New Relic. Enhance performance monitoring and gain insights for smarter decision-making.

How to Connect Google Cloud AI to New Relic: a Simple Guide

 

Set Up Google Cloud AI Credentials

 

  • Navigate to the Google Cloud Console: Google Cloud Console
  •  

  • Enable the APIs you need, such as the Cloud Vision API, Natural Language API, or others.
  •  

  • Go to "Credentials" and select "Create credentials" to set up a new service account key. Be sure to download the JSON key file as you will need this for accessing the API.

 

Install Required Libraries

 

  • Ensure you have Python installed as we will be using Python for demonstration. Use the following command to install the necessary Google Cloud library:

 

pip install google-cloud

 

Create a New Relic Account and Set it Up

 

  • Sign up for a New Relic account if you do not have one: New Relic Registration.
  •  

  • Install the New Relic agent appropriate for your platform, be it for applications, server environment, or infrastructure monitoring.
  •  

  • Follow the installation instructions specific to your setup. For a simple Python application, you might do:

 

pip install newrelic

 

Configure New Relic

 

  • Use your New Relic license key to configure your applications. Modify the configuration file (usually `newrelic.ini`) with your application name and license key:

 

[common]
app_name = My Application Name
license_key = YOUR_NEW_RELIC_LICENSE_KEY

 

Integrate Google Cloud AI with New Relic

 

  • Create a script or service to interact with Google Cloud AI APIs using the credentials JSON key you downloaded earlier.
  •  

  • Import the needed libraries and set up the service as follows for a Python application:

 

from google.cloud import vision
import newrelic.agent

newrelic.agent.initialize('newrelic.ini')

def detect_text(image_path):
    client = vision.ImageAnnotatorClient.from_service_account_json('path/to/your/credentials.json')
    with open(image_path, 'rb') as image_file:
        content = image_file.read()
    image = vision.Image(content=content)
    response = client.text_detection(image=image)
    annotations = response.text_annotations
    return annotations

 

Send Custom Events or Metrics to New Relic

 

  • Create custom events in New Relic to monitor specific data points from Google Cloud AI services:

 

def report_custom_event(annotations):
    # This function logs custom events to New Relic
    for annotation in annotations:
        newrelic.agent.record_custom_event('GoogleCloudAI', {
            'description': annotation.description,
            'vertices': annotation.bounding_poly.vertices
        })

 

Deploy & Monitor

 

  • Deploy your integration to your environment.
  •  

  • Monitor how data flows from Google Cloud AI to New Relic by checking the New Relic dashboard for custom events or metrics.
  •  

  • Refine based on insights and any bottlenecks or anomalies.

 

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How to Use Google Cloud AI with New Relic: Usecases

 

Enhancing Retail Customer Experience with Google Cloud AI and New Relic

 

  • Leverage **Google Cloud AI** by using its natural language processing capabilities to analyze customer feedback from various channels such as emails, social media, and online reviews.
  •  

  • Utilize **Google Cloud's Vision AI** to process visual data from in-store cameras, allowing analysis of customer behavior patterns for better space and product placement.
  •  

  • Integrate **New Relic** to monitor application performance, ensuring that all AI-powered analytics tools and customer-facing applications are running efficiently and without downtime.
  •  

 

Real-time Data Processing

 

  • Deploy **Google Cloud Functions** to handle real-time data processing tasks triggered by customer interactions, such as changes in shopping patterns or sudden feedback surges.
  •  

  • Set up **New Relic alerts** to immediately notify IT teams of any anomalies in data processing times, ensuring quick responses to potential issues.
  •  

 

Improving Decision-Making with Predictive Modeling

 

  • Use **Google Cloud AI's predictive modeling features** to forecast inventory needs based on historical sales data and identified trends from customer intelligence.
  •  

  • Implement **New Relic monitoring dashboards** to visualize performance metrics, identifying which predictive models deliver the most accurate forecasts and optimal application efficiency.
  •  

 

Seamless Integration for Optimized Operations

 

  • Create a **synchronized data pipeline** using Google Cloud's BigQuery and New Relic's data processing capabilities to maintain a unified view of operations and customer interactions.
  •  

  • Leverage the **API integration** capabilities of both platforms to automate workflows and streamline back-office operations, reducing manual intervention and increasing operational efficiency.
  •  

 

Security and Compliance Monitoring

 

  • Ensure all customer data processed through **Google Cloud AI** adheres to industry regulations by utilizing New Relic's security monitoring tools to track data access and transaction logs.
  •  

  • Enable **real-time compliance checks** across applications monitored by New Relic, ensuring that changes in customer data processing methods remain compliant with changing guidelines.
  •  

 

 

Optimizing Healthcare Services with Google Cloud AI and New Relic

 

  • Utilize Google Cloud AI's Natural Language Processing capabilities to analyze patient sentiment from feedback forms, online reviews, and social media to enhance patient care quality.
  •  

  • Employ Google Cloud's Vision AI to assist in medical image processing, aiding in diagnostics and patient treatment plans by identifying patterns in radiology images.
  •  

  • Leverage New Relic's monitoring tools to ensure the performance and reliability of AI-driven diagnostic applications, minimizing potential downtime in critical healthcare processes.
  •  

 

Real-time Patient Monitoring and Alerting

 

  • Deploy Google Cloud Functions for real-time data processing from wearable health devices, generating immediate insights and health status alerts for patients.
  •  

  • Use New Relic's alerting system to notify healthcare providers of any deviations in patient health data, facilitating prompt medical intervention.
  •  

 

Predictive Analytics for Resource Management

 

  • Implement Google Cloud AI's predictive analytics to forecast patient influx and resource needs, ensuring adequate staffing and supply levels at healthcare facilities.
  •  

  • Utilize New Relic's performance dashboards to monitor and visualize predictions, enhancing decision-making for healthcare administrators by displaying the accuracy and impact of forecasting.
  •  

 

Data Integration and Operational Efficiency

 

  • Create a comprehensive data integration framework utilizing Google Cloud's BigQuery, combined with New Relic's data monitoring capabilities for streamlined healthcare operations.
  •  

  • Enable seamless API integration between the platforms to automate administrative workflows, reducing the burden on healthcare staff and improving overall service delivery efficiency.
  •  

 

Enhanced Security and Regulatory Compliance

 

  • Ensure all patient data processed via Google Cloud AI is secure and compliant, using New Relic's monitoring solutions to oversee data access and transaction logs thoroughly.
  •  

  • Conduct real-time compliance verification with New Relic across all applications, ensuring that patient data handling practices consistently meet healthcare regulations.
  •  

 

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Troubleshooting Google Cloud AI and New Relic Integration

How to send Google Cloud AI metrics to New Relic?

 

Integrate Google Cloud AI with New Relic

 

  • Ensure Google Cloud monitoring setup is completed. Install and configure the Cloud Monitoring agent on your virtual machine instances; this helps collect system and application metrics.
  •  

  • Create a Pub/Sub topic on Google Cloud to stream metrics. Use Google Cloud's Operations suite to export these to the topic.
  •  

  • In New Relic, use the New Relic One platform to set up a custom dashboard or alerts for the metrics you plan to monitor.

 

Export Metrics to Pub/Sub

 

  • Go to the Cloud Console, select your Google Cloud project, and navigate to Operations   > Metrics Explorer. Use the Export feature to create a new stream to your Pub/Sub topic.

 

from google.cloud import pubsub_v1

project_id = "your-gcp-project-id"
topic_id = "your-pubsub-topic-id"

publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path(project_id, topic_id)

data = "Your metric data"
publisher.publish(topic_path, data.encode("utf-8"))

 

Connect Pub/Sub to New Relic

 

  • Create a New Relic account if you don’t have one. Obtain an API key and use it with New Relic’s API to send customized JSON payloads representing your cloud metrics.
  •  

  • Consider developing an application using New Relic's Flex integration or other SDKs to automate this connection further.

 

Why is my Google Cloud AI data not appearing in New Relic?

 

Verify Data Reports

 

  • Ensure Google Cloud AI is correctly reporting data. Check if the data pipeline to New Relic is properly configured and active.
  •  

  • Confirm that AI-related metrics are being captured in Google Cloud Monitoring and Logs.

 

Configure New Relic

 

  • Ensure New Relic dashboards are set to receive AI data. Check data sources and connections within New Relic for active statuses.
  •  

  • Review New Relic configuration files and ensure they're set to accept data from Google Cloud AI's APIs or export mechanisms.

 

Check Integrations

 

  • Verify Google Cloud Platform (GCP) and New Relic integrations. Use required APIs and authentication keys for secure data transfer.
  •  

  • Position third-party connectors properly. Update software as regulated by changes in API versions or library updates.

 

# Example to check integration
from google.cloud import monitoring_v3

client = monitoring_v3.MetricServiceClient()
project_name = f"projects/YOUR_PROJECT_ID"
metrics = client.list_metric_descriptors(name=project_name)
for metric in metrics:
    print(metric.type)

How do I troubleshoot Google Cloud AI monitoring in New Relic?

 

Ensure API Integration

 

  • Verify that Google Cloud AI Monitoring API is enabled and properly integrated with New Relic.
  • Check if New Relic has the necessary permissions for accessing the Google Cloud services.

 

Inspect Configuration Settings

 

  • Review configuration settings in both Google Cloud and New Relic dashboards for any discrepancies.
  • Ensure your Google project ID and other credentials are correctly set up.

 

Analyze Network Connectivity

 

  • Use diagnostic tools to confirm there's no network blockage between Google Cloud and New Relic.
  • Verify that the firewall rules permit traffic on essential ports.

 

Review Logs and Alerts

 

  • Examine logs in the New Relic dashboard to identify errors or warnings related to Google Cloud Metrics.

 

gcloud logging read "resource.type=global AND severity>=ERROR"

 

Update Dependencies

 

  • Make sure all libraries and SDKs are up-to-date.

 

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