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|  How to Integrate Amazon AI with New Relic

How to Integrate Amazon AI with New Relic

January 24, 2025

Discover step-by-step how to seamlessly integrate Amazon AI with New Relic to enhance monitoring and analytics for your applications.

How to Connect Amazon AI to New Relic: a Simple Guide

 

Prerequisites

 

  • Create an Amazon Web Services (AWS) account if you don’t have one, and set up your AI services such as Amazon Rekognition, Lex, or Comprehend.
  •  

  • Create a New Relic account and ensure you have the necessary permissions to install agents and configure integrations.

 

Set Up AWS SDK

 

  • Choose the AWS SDK language that suits your application (e.g., Python, JavaScript, Java).
  •  

  • Install the AWS SDK for your chosen language. Below is an example for Node.js:

 

npm install aws-sdk

 

Configure AWS Credentials

 

  • Configure your AWS credentials to allow your application to communicate with AWS services.
  •  

  • Create a new IAM role in AWS with policies that allow access to the AI services you plan to use.
  •  

  • Use environment variables, AWS credentials file, or AWS Secrets Manager to store your credentials securely. Here’s an example using environment variables:

 

export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key

 

Integrate Amazon AI into Your Application

 

  • Use the AWS SDK to call AI services. Here’s a JavaScript example for Amazon Rekognition:

 

const AWS = require('aws-sdk');
const rekognition = new AWS.Rekognition();

const params = {
  Image: {
    S3Object: {
      Bucket: 'your-bucket',
      Name: 'your-image.jpg'
    }
  },
  MaxLabels: 10
};

rekognition.detectLabels(params, (err, data) => {
  if (err) console.log(err, err.stack);
  else     console.log(data);
});

 

Install New Relic Agent

 

  • Choose the New Relic agent that matches your application stack (e.g., Java, Node.js, Python).
  •  

  • Install the New Relic agent by following the provided documentation. Here’s an example for Node.js:

 

npm install newrelic

 

Configure New Relic

 

  • Update the New Relic agent configuration file (e.g., newrelic.js for Node.js). Add your New Relic license key and application name:

 

exports.config = {
  app_name: ['Your Application'],
  license_key: 'your_new_relic_license_key',
  logging: {
    level: 'info'
  }
}

 

Instrument Your Application

 

  • Use New Relic's APIs to instrument relevant parts of your code, particularly where you are making AWS SDK calls. In Node.js, start by requiring the New Relic agent at the top of your application:

 

require('newrelic');

 

  • Add custom metrics or trace information using New Relic’s APIs:

 

const newrelic = require('newrelic');

newrelic.startBackgroundTransaction('customTransaction', function() {
  // your AWS SDK call
  newrelic.endTransaction();
});

 

Monitor and Analyze

 

  • Log in to your New Relic account to monitor the performance of AWS AI services integrated into your application.
  •  

  • Create dashboards and alerts to analyze AWS service usage, performance trends, and detect anomalies in real-time.

 

Maintain and Update

 

  • Regularly update AWS SDK and New Relic agents to their latest versions to ensure optimal compatibility and security.
  •  

  • Refine your monitoring strategy based on the insights gained from New Relic dashboards and reports to enhance system performance.

 

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How to Use Amazon AI with New Relic: Usecases

 

Enhancing E-commerce Platform Monitoring and Personalization

 

  • Utilize Amazon AI's innovative recommendation algorithms to enhance personalization on your e-commerce platform. Leverage the machine learning models to predict customer preferences and deliver personalized shopping experiences.
  •  

  • Integrate New Relic to monitor application performance, ensuring that all AI-driven recommendations are delivered seamlessly and without delay.
  •  

  • Analyze user interaction data using Amazon AI insights to understand browsing patterns, purchase behaviors, and identify trends that can enhance the user experience.
  •  

  • Set up New Relic dashboards to visualize key performance metrics, such as response times for AI-generated recommendations, server loads, and error rates.
  •  

  • Employ New Relic alerts to receive immediate notifications about performance bottlenecks or downtimes, allowing rapid troubleshooting to maintain optimal user engagement.
  •  

 

Implementation Steps

 

  • Deploy Amazon Personalize on your AWS setup to begin customizing the user experience in real time. Feed customer interaction data into the service to train the AI models.
  •  

  • Incorporate New Relic agents across your e-commerce application's infrastructure to capture detailed insights into system performance and user interactions with personalized recommendations.
  •  

  • Configure New Relic custom dashboards to track the impact of AI personalization on customer engagement metrics like click-through rates and conversion rates.
  •  

  • Utilize both Amazon CloudWatch and New Relic to maintain an end-to-end view of your system’s health and performance, integrating logs and metrics for comprehensive monitoring.
  •  

  • Iterate and refine the recommendation models using insights from New Relic reports and Amazon AI analytics to continuously improve the relevance and accuracy of the shopping experience.
  •  

 


# Implement Amazon Personalize
aws personalize import-data

# Install New Relic on the server
curl -Ls https://download.newrelic.com/infrastructure_agent/gpg/newrelic-infra.gpg | sudo apt-key add -

 

 

Advanced Web Application Optimization Using Amazon AI and New Relic

 

  • Deploy Amazon AI's predictive analytics to forecast server load and optimize resource allocation dynamically. Use these insights to prevent downtime and enhance user experience without over-provisioning resources.
  •  
  • Leverage New Relic's detailed monitoring capabilities to track the application's health, specifically focusing on AI-driven adjustments and their impact on application responsiveness and stability.
  •  
  • Utilize Amazon AI's machine learning models for adaptive content delivery on your web application. Analyze user data to predict content engagement and improve content delivery efficiency by preloading popular assets during peak times.
  •  
  • Create New Relic dashboards that display AI predictions against actual server performance metrics, such as CPU usage, memory load, and request handling times, to assess model accuracy in real time.
  •  
  • Configure New Relic alerts to ensure timely intervention in the case of deviations from predicted performance benchmarks, automatically triggering scaling adjustments to handle unexpected traffic spikes.
  •  

 

Integration Workflow

 

  • Start by integrating Amazon SageMaker in your application infrastructure to build, train, and deploy machine learning models that predict usage patterns and recommend resource configurations.
  •  
  • Incorporate New Relic agents across all application environments to capture detailed performance data, essential for fine-tuning Amazon AI models based on real-world usage.
  •  
  • Set up comprehensive New Relic views to correlate AI-driven optimizations, such as autoscaling decision impacts, with customer satisfaction indicators like load time and session length.
  •  
  • Use Amazon CloudWatch in tandem with New Relic to consolidate logs and metrics, providing an integrated monitoring setup that facilitates seamless incident resolution and continuous application improvement.
  •  
  • Regularly update predictions and deployment strategies through feedback loops that include both performance insights from New Relic and data analysis from Amazon AI, ensuring adaptive optimization.
  •  

 

```shell

Install Amazon SageMaker

pip install sagemaker

Deploy New Relic agent

curl -Ls https://download.newrelic.com/infrastructure\_agent/gpg/newrelic-infra.gpg | sudo apt-key add -
```

 

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Troubleshooting Amazon AI and New Relic Integration

How to integrate Amazon SageMaker with New Relic for real-time monitoring?

 

Set Up Amazon SageMaker

 

  • Ensure your SageMaker instance is running. Use SageMaker API or AWS Management Console for status check.

 

Install and Configure New Relic Agent

 

  • Log in to your SageMaker instance and install the New Relic Python agent using pip:
pip install newrelic

 

  • Get your New Relic license key from the New Relic account settings and configure the agent:
import newrelic.agent
newrelic.agent.initialize('/path/to/newrelic.ini')

 

Integrate SageMaker with New Relic

 

  • Add the New Relic decorator to your SageMaker endpoints to monitor request handling:
from newrelic.agent import function_trace

@function_trace()
def predict(input_data):
    # Your prediction logic here

 

  • Deploy your updated SageMaker model with New Relic integration.

 

Monitor in New Relic

 

  • Access your application's dashboard in New Relic to visualize metrics and set real-time alerts.

 

What are common errors when connecting Amazon Rekognition to New Relic?

 

Common Errors

 

  • Authentication Failures: Ensure correct IAM roles and permissions in AWS for Rekognition. Verify that New Relic's credentials are properly set.
  •  

  • Latency Issues: Network latency can cause retries or timeouts. Consider using AWS regions close to your New Relic resources.
  •  

  • Data Format: Ensure data sent to Rekognition is in a supported format. Image inputs must adhere to Rekognition's API specifications. Mismatches cause processing errors.

 

Troubleshooting Code Example

 

import boto3
from newrelic import agent

def analyze_image(image):
    try:
        client = boto3.client('rekognition')
        response = client.detect_labels(Image={'Bytes': image})
        agent.record_custom_event('RekognitionAnalysis', response)
    except client.exceptions.InvalidParameterException as e:
        print(f"Invalid parameters: {e}")
        agent.record_exception()
    except Exception as e:
        print(f"Error connecting to Rekognition: {e}")
        agent.record_exception()

# Example image input
with open('image.jpg', 'rb') as image_file:
    content = image_file.read()
    analyze_image(content)

 

How do I visualize Amazon AI metric data in New Relic dashboards?

 

Integrate Amazon AI Metric Data with New Relic

 

  • Use Amazon's cloud services to expose metric data via Amazon CloudWatch. Configure your AI service to send relevant metrics to CloudWatch.
  •  

  • Set up a CloudWatch integration with New Relic using the New Relic AWS CloudWatch integration. This allows New Relic to ingest your Amazon AI metric data.

 

Create a New Relic Dashboard

 

  • Log into New Relic and navigate to the Dashboards section. Click “Create a dashboard” to start a new visualization.
  •  

  • Use New Relic's query language, NRQL, to fetch your Amazon AI metrics. Example NRQL query for visualizing CPU usage:
  •  

    SELECT average(cpuUtilization) FROM Metric WHERE provider = 'Amazon' TIMESERIES
    

     

 

Add Visualizations

 

  • Select the visualization type (e.g., line chart, pie chart) that best represents your data.
  •  

  • Drag and drop widgets into your dashboard, then apply your NRQL queries to populate them with data.

 

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