#aws-lambda

[cybereliasacademy]

Performance Best Practices Using Java and AWS Lambda: Combinations

Subscribe .tb0e30274-9552-4f02-8737-61d4b7a7ad49 { color: #fff; background: #222; border: 1px solid transparent; border-radius: undefinedpx; padding: 8px 21px; } .tb0e30274-9552-4f02-8737-61d4b7a7ad49.place-top { margin-top: -10px; } .tb0e30274-9552-4f02-8737-61d4b7a7ad49.place-top::before { content: “”; background-color: inherit; position: absolute; z-index: 2; width: 20px; height: 12px; }…

[mechahero]

//Lambda would probably like to collect keychains. Cute ones, ones with fun designs, ones that are shaped like miniature game consoles or game cases. He would probably put some of them on a belt loop or wallet chain on his jeans because he thinks it would look cute. You could probably hear him coming from a mile away with the amount of keychains jingling though.

[juggalogojackerbox]

Cringetober Day 1 - Heterochromia

First drawing of the month lets GO, felt like drawing λ was a good choice for this one %] (especially given how important his multicolored eyes are in his backstory, I'm givin' my boy his chance to shine here he deserves it- but im goin' on a tangent now my bad)

[futuristicbugpvtltd]

Serverless Computing: Streamlining Web Application Deployment

[codebriefly]

New Post has been published on https://codebriefly.com/how-to-handle-bounce-and-complaint-notifications-in-aws-ses/

How to handle Bounce and Complaint Notifications in AWS SES with SNS, SQS, and Lambda

In this article, we will discuss “how to handle complaints and bounce in AWS SES using SNS, SQS, and Lambda”. Amazon Simple Email Service (SES) is a powerful tool for sending emails, but handling bounce and complaint notifications is crucial to maintaining a good sender reputation. AWS SES provides mechanisms to capture these notifications via Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), and AWS Lambda.

This article will guide you through setting up this pipeline and provide Python code to process bounce and complaint notifications and add affected recipients to the AWS SES suppression list.

Table of Contents

Toggle

Architecture Overview

Step 1: Configure AWS SES to Send Notifications

Step 2: Subscribe SQS Queue to SNS Topic

Step 3: Create a Lambda Function to Process Notifications

Python Code for AWS Lambda

Step 4: Deploy the Lambda Function

Step 5: Test the Pipeline

Conclusion

Architecture Overview

SES Sends Emails: AWS SES is used to send emails.

SES Triggers SNS: SES forwards bounce and complaint notifications to an SNS topic.

SNS Delivers to SQS: SNS publishes these messages to an SQS queue.

Lambda Processes Messages: A Lambda function reads messages from SQS, identifies bounced and complained addresses, and adds them to the SES suppression list.

Step 1: Configure AWS SES to Send Notifications

Go to the AWS SES console.

Navigate to Email Identities and select the verified email/domain.

Under the Feedback Forwarding section, set up SNS notifications for Bounces and Complaints.

Create an SNS topic and subscribe an SQS queue to it.

Step 2: Subscribe SQS Queue to SNS Topic

Create an SQS queue.

In the SNS topic settings, subscribe the SQS queue.

Modify the SQS queue’s access policy to allow SNS to send messages.

Step 3: Create a Lambda Function to Process Notifications

The Lambda function reads bounce and complaint notifications from SQS and adds affected email addresses to the AWS SES suppression list.

Python Code for AWS Lambda

import json import boto3 sqs = boto3.client('sqs') sesv2 = boto3.client('sesv2') # Replace with your SQS queue URL SQS_QUEUE_URL = "https://sqs.us-east-1.amazonaws.com/YOUR_ACCOUNT_ID/YOUR_QUEUE_NAME" def lambda_handler(event, context): messages = receive_sqs_messages() for message in messages: process_message(message) delete_sqs_message(message['ReceiptHandle']) return 'statusCode': 200, 'body': 'Processed messages successfully' def receive_sqs_messages(): response = sqs.receive_message( QueueUrl=SQS_QUEUE_URL, MaxNumberOfMessages=10, WaitTimeSeconds=5 ) return response.get("Messages", []) def process_message(message): body = json.loads(message['Body']) notification = json.loads(body['Message']) if 'bounce' in notification: bounced_addresses = [rec['emailAddress'] for rec in notification['bounce']['bouncedRecipients']] add_to_suppression_list(bounced_addresses) if 'complaint' in notification: complained_addresses = [rec['emailAddress'] for rec in notification['complaint']['complainedRecipients']] add_to_suppression_list(complained_addresses) def add_to_suppression_list(email_addresses): for email in email_addresses: sesv2.put_suppressed_destination( EmailAddress=email, Reason='BOUNCE' # Use 'COMPLAINT' for complaint types ) print(f"Added email to SES suppression list") def delete_sqs_message(receipt_handle): sqs.delete_message( QueueUrl=SQS_QUEUE_URL, ReceiptHandle=receipt_handle )

Step 4: Deploy the Lambda Function

Go to the AWS Lambda console.

Create a new Lambda function.

Attach the necessary IAM permissions:

Read from SQS

Write to SES suppression list

Deploy the function and configure it to trigger from the SQS queue.

Step 5: Test the Pipeline

Send a test email using SES to an invalid address.

Check the SQS queue for incoming messages.

Verify that the email address is added to the SES suppression list.

Conclusion

In this article, we are discussing “How to handle Bounce and Complaint Notifications in AWS SES with SNS, SQS, and Lambda”. This setup ensures that bounce and complaint notifications are handled efficiently, preventing future emails to problematic addresses and maintaining a good sender reputation. By leveraging AWS Lambda, SQS, and SNS, you can automate the process and improve email deliverability.

Keep learning and stay safe 🙂

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Basic Understanding on AWS Lambda

[awsconsultingservices]

AWS Lambda Explained: Use Cases, Security Considerations, Performance, and Cost Insights

[codeonedigest]

AWS Lambda with EventBridge Service | Step-by-Step Tutorial Full Video Link -                https://youtu.be/ShrlSJ5S3yg Check out this new video on the CodeOneDigest YouTube channel! Learn how to setup aws lambda function? How to invoke lambda function using eventbridge event?@codeonedigest @awscloud @AWSCloudIndia @AWS_Edu @AWSSupport @AWS_Gov @AWSArchitecture

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Using AWS Lambda for Serverless Computing: A Real-World Example

In recent years, serverless computing has become one of the most transformative trends in cloud computing. AWS Lambda, Amazon Web Services’ serverless compute service, has emerged as one of the key tools for building scalable, event-driven applications without the need to manage servers. In this post, we’ll walk through a real-world example of using AWS Lambda for serverless computing, highlighting the key benefits and how you can use Lambda to simplify your infrastructure.

What is AWS Lambda?

AWS Lambda is a compute service that allows you to run code without provisioning or managing servers. You upload your code (usually as a function), set the trigger, and Lambda takes care of everything else—auto-scaling, high availability, and even fault tolerance. This makes it an ideal solution for building microservices, processing data streams, automating tasks, and more.

Real-World Example: Building an Image Resizing Service

Let’s dive into a practical example of how AWS Lambda can be used to build a serverless image resizing service. Suppose you run a website where users upload images, and you want to automatically resize these images for different use cases—like thumbnails, profile pictures, and full-size versions.

Step 1: Create an S3 Bucket for Image Storage

The first step is to create an Amazon S3 bucket, where users will upload their images. S3 is an object storage service that is highly scalable and integrates seamlessly with AWS Lambda.

Step 2: Create the Lambda Function

Next, you’ll create a Lambda function that performs the image resizing. The code for this function is typically written in Python, Node.js, or another supported runtime. Here's an example Python function that resizes an image using the Pillow library:

import boto3

from PIL import Image

import io

s3 = boto3.client('s3')

def lambda_handler(event, context):

    # Get the S3 bucket and object key from the event

    bucket_name = event['Records'][0]['s3']['bucket']['name']

    object_key = event['Records'][0]['s3']['object']['key']

    # Download the image file from S3

    img_obj = s3.get_object(Bucket=bucket_name, Key=object_key)

    img_data = img_obj['Body'].read()

    img = Image.open(io.BytesIO(img_data))

    # Resize the image

    img_resized = img.resize((128, 128))  # Resize to 128x128 pixels

    # Save the resized image back to S3

    out_key = f"resized/{object_key}"

    out_buffer = io.BytesIO()

    img_resized.save(out_buffer, 'JPEG')

    out_buffer.seek(0)

    s3.put_object(Bucket=bucket_name, Key=out_key, Body=out_buffer)

    return {'statusCode': 200, 'body': 'Image resized successfully'}

This function does the following:

Downloads the uploaded image from the S3 bucket.

Resizes the image to 128x128 pixels.

Uploads the resized image back to the S3 bucket under a new path (e.g., resized/{object_key}).

Step 3: Set Up an S3 Event Trigger

AWS Lambda works seamlessly with other AWS services, like S3. To automate the image resizing process, you can set up an S3 event notification that triggers your Lambda function every time a new image is uploaded to the bucket. This is configured within the S3 console by adding an event notification that calls your Lambda function when an object is created.

Step 4: Testing the Lambda Function

Now that the Lambda function is set up and triggered by S3 events, you can test it by uploading an image to the S3 bucket. Once the image is uploaded, Lambda will automatically process the image, resize it, and store it in the designated S3 path.

Step 5: Monitor and Scale Automatically

One of the biggest advantages of using AWS Lambda is that you don’t have to worry about scaling. Lambda automatically scales to handle the volume of events, and you only pay for the compute time you use (in terms of requests and execution duration). AWS also provides monitoring and logging via Amazon CloudWatch, so you can easily track the performance of your Lambda function and troubleshoot if needed.

Key Benefits of Using AWS Lambda for Serverless Computing

Cost Efficiency: With AWS Lambda, you only pay for the execution time, meaning you don’t incur costs for idle resources. This is ideal for applications with variable or unpredictable workloads.

Auto-Scaling: Lambda automatically scales to handle an increasing number of events, without needing you to manually adjust infrastructure. This makes it well-suited for burst workloads, like processing thousands of images uploaded in a short period.

No Server Management: You don’t need to manage the underlying infrastructure. AWS handles provisioning, patching, and scaling of the servers, allowing you to focus on your code and business logic.

Event-Driven: Lambda integrates with many AWS services like S3, DynamoDB, SNS, and API Gateway, enabling you to build event-driven architectures without complex setups.

Quick Deployment: With Lambda, you can deploy your application faster, as there’s no need to worry about provisioning servers, load balancing, or scaling. Upload your code, set the trigger, and it’s ready to go.

Conclusion

AWS Lambda simplifies serverless application development by removing the need to manage infrastructure and enabling automatic scaling based on demand. In our image resizing example, Lambda not only reduces the complexity of managing servers but also makes the application more cost-effective and scalable. Whether you’re building a microservice, automating tasks, or handling real-time data streams, AWS Lambda is a powerful tool that can help you develop modern, cloud-native applications with ease.

By embracing serverless computing with AWS Lambda, you can build highly scalable, efficient, and cost-effective applications that are ideal for today's fast-paced, cloud-driven world.

To know more about AWS Lambda Enroll Now:

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[mechahero]

//Everything's fine for demons that run amuck looking to take over Earth until they bump into the guy that would gladly tear them apart with his teeth and hands.

[insertpinkchiphere]

@dragvnsovl asked- [ catch ]  to catch my muse masturbating //Gogeta catching Lambs! smutty interactions inbox memes

A groan slips out through clenched teeth. One he hadn't managed to choke down as he touches himself. There sits Lambda, seated on his knees, his arms behind himself to keep himself propped up while his legs parted far enough apart to be comfortable while allowing himself to slide his fingers inside without much trouble. Or rather, a copy of said fingers with the flash of blue that catches his eye as they pull out.

"Mmh!" He bites down on his lip, attempting to muffle himself. A task made all the harder by the pleasantly relentless pace he's set for them. So distracted was he, that he hadn't even noticed when the door had been knocked on. Or when it had opened. It isn't until the light flicks on when his attention is truly caught and Lambda's confusedly turning his gaze over to the door. And when he does... he shrieks.

One part scared senseless, one part embarrassed. His nanites disappearing as quickly as they came. "Wh- ummm- Ve-" No, that's not quite right. "Go-"" His head is whipping about, looking for something, anything on the bed to cover himself with. He settles for covering his chest with one of his arms and his other hand going to cover his crotch. His gaze however is doing its level best to burn a hole through Gogeta.

"You! Fucking... learn how to knock!", Lambda shouts at practically the top of his lungs. Though, really, it's to cover how utterly mortified he is to have been caught like this. If the bright blue clouding his face hadn't already been indication enough.

[jcmarchi]

Transforming LLM Performance: How AWS’s Automated Evaluation Framework Leads the Way

New Post has been published on https://thedigitalinsider.com/transforming-llm-performance-how-awss-automated-evaluation-framework-leads-the-way/

Transforming LLM Performance: How AWS’s Automated Evaluation Framework Leads the Way

Large Language Models (LLMs) are quickly transforming the domain of Artificial Intelligence (AI), driving innovations from customer service chatbots to advanced content generation tools. As these models grow in size and complexity, it becomes more challenging to ensure their outputs are always accurate, fair, and relevant.

To address this issue, AWS’s Automated Evaluation Framework offers a powerful solution. It uses automation and advanced metrics to provide scalable, efficient, and precise evaluations of LLM performance. By streamlining the evaluation process, AWS helps organizations monitor and improve their AI systems at scale, setting a new standard for reliability and trust in generative AI applications.

Why LLM Evaluation Matters

LLMs have shown their value in many industries, performing tasks such as answering questions and generating human-like text. However, the complexity of these models brings challenges like hallucinations, bias, and inconsistencies in their outputs. Hallucinations happen when the model generates responses that seem factual but are not accurate. Bias occurs when the model produces outputs that favor certain groups or ideas over others. These issues are especially concerning in fields like healthcare, finance, and legal services, where errors or biased results can have serious consequences.

It is essential to evaluate LLMs properly to identify and fix these issues, ensuring that the models provide trustworthy results. However, traditional evaluation methods, such as human assessments or basic automated metrics, have limitations. Human evaluations are thorough but are often time-consuming, expensive, and can be affected by individual biases. On the other hand, automated metrics are quicker but may not catch all the subtle errors that could affect the model’s performance.

For these reasons, a more advanced and scalable solution is necessary to address these challenges. AWS’s Automated Evaluation Framework provides the perfect solution. It automates the evaluation process, offering real-time assessments of model outputs, identifying issues like hallucinations or bias, and ensuring that models work within ethical standards.

AWS’s Automated Evaluation Framework: An Overview

AWS’s Automated Evaluation Framework is specifically designed to simplify and speed up the evaluation of LLMs. It offers a scalable, flexible, and cost-effective solution for businesses using generative AI. The framework integrates several core AWS services, including Amazon Bedrock, AWS Lambda, SageMaker, and CloudWatch, to create a modular, end-to-end evaluation pipeline. This setup supports both real-time and batch assessments, making it suitable for a wide range of use cases.

Key Components and Capabilities

Amazon Bedrock Model Evaluation

At the foundation of this framework is Amazon Bedrock, which offers pre-trained models and powerful evaluation tools. Bedrock enables businesses to assess LLM outputs based on various metrics such as accuracy, relevance, and safety without the need for custom testing systems. The framework supports both automatic evaluations and human-in-the-loop assessments, providing flexibility for different business applications.

LLM-as-a-Judge (LLMaaJ) Technology

A key feature of the AWS framework is LLM-as-a-Judge (LLMaaJ), which uses advanced LLMs to evaluate the outputs of other models. By mimicking human judgment, this technology dramatically reduces evaluation time and costs, up to 98% compared to traditional methods, while ensuring high consistency and quality. LLMaaJ evaluates models on metrics like correctness, faithfulness, user experience, instruction compliance, and safety. It integrates effectively with Amazon Bedrock, making it easy to apply to both custom and pre-trained models.

Customizable Evaluation Metrics

Another prominent feature is the framework’s ability to implement customizable evaluation metrics. Businesses can tailor the evaluation process to their specific needs, whether it is focused on safety, fairness, or domain-specific accuracy. This customization ensures that companies can meet their unique performance goals and regulatory standards.

Architecture and Workflow

The architecture of AWS’s evaluation framework is modular and scalable, allowing organizations to integrate it easily into their existing AI/ML workflows. This modularity ensures that each component of the system can be adjusted independently as requirements evolve, providing flexibility for businesses at any scale.

Data Ingestion and Preparation

The evaluation process begins with data ingestion, where datasets are gathered, cleaned, and prepared for evaluation. AWS tools such as Amazon S3 are used for secure storage, and AWS Glue can be employed for preprocessing the data. The datasets are then converted into compatible formats (e.g., JSONL) for efficient processing during the evaluation phase.

Compute Resources

The framework uses AWS’s scalable compute services, including Lambda (for short, event-driven tasks), SageMaker (for large and complex computations), and ECS (for containerized workloads). These services ensure that evaluations can be processed efficiently, whether the task is small or large. The system also uses parallel processing where possible, speeding up the evaluation process and making it suitable for enterprise-level model assessments.

Evaluation Engine

The evaluation engine is a key component of the framework. It automatically tests models against predefined or custom metrics, processes the evaluation data, and generates detailed reports. This engine is highly configurable, allowing businesses to add new evaluation metrics or frameworks as needed.

Real-Time Monitoring and Reporting

The integration with CloudWatch ensures that evaluations are continuously monitored in real-time. Performance dashboards, along with automated alerts, provide businesses with the ability to track model performance and take immediate action if necessary. Detailed reports, including aggregate metrics and individual response insights, are generated to support expert analysis and inform actionable improvements.

How AWS’s Framework Enhances LLM Performance

AWS’s Automated Evaluation Framework offers several features that significantly improve the performance and reliability of LLMs. These capabilities help businesses ensure their models deliver accurate, consistent, and safe outputs while also optimizing resources and reducing costs.

Automated Intelligent Evaluation

One of the significant benefits of AWS’s framework is its ability to automate the evaluation process. Traditional LLM testing methods are time-consuming and prone to human error. AWS automates this process, saving both time and money. By evaluating models in real-time, the framework immediately identifies any issues in the model’s outputs, allowing developers to act quickly. Additionally, the ability to run evaluations across multiple models at once helps businesses assess performance without straining resources.

Comprehensive Metric Categories

The AWS framework evaluates models using a variety of metrics, ensuring a thorough assessment of performance. These metrics cover more than just basic accuracy and include:

Accuracy: Verifies that the model’s outputs match expected results.

Coherence: Assesses how logically consistent the generated text is.

Instruction Compliance: Checks how well the model follows given instructions.

Safety: Measures whether the model’s outputs are free from harmful content, like misinformation or hate speech.

In addition to these, AWS incorporates responsible AI metrics to address critical issues such as hallucination detection, which identifies incorrect or fabricated information, and harmfulness, which flags potentially offensive or harmful outputs. These additional metrics are essential for ensuring models meet ethical standards and are safe for use, especially in sensitive applications.

Continuous Monitoring and Optimization

Another essential feature of AWS’s framework is its support for continuous monitoring. This enables businesses to keep their models updated as new data or tasks arise. The system allows for regular evaluations, providing real-time feedback on the model’s performance. This continuous loop of feedback helps businesses address issues quickly and ensures their LLMs maintain high performance over time.

Real-World Impact: How AWS’s Framework Transforms LLM Performance

AWS’s Automated Evaluation Framework is not just a theoretical tool; it has been successfully implemented in real-world scenarios, showcasing its ability to scale, enhance model performance, and ensure ethical standards in AI deployments.

Scalability, Efficiency, and Adaptability

One of the major strengths of AWS’s framework is its ability to efficiently scale as the size and complexity of LLMs grow. The framework employs AWS serverless services, such as AWS Step Functions, Lambda, and Amazon Bedrock, to automate and scale evaluation workflows dynamically. This reduces manual intervention and ensures that resources are used efficiently, making it practical to assess LLMs at a production scale. Whether businesses are testing a single model or managing multiple models in production, the framework is adaptable, meeting both small-scale and enterprise-level requirements.

By automating the evaluation process and utilizing modular components, AWS’s framework ensures seamless integration into existing AI/ML pipelines with minimal disruption. This flexibility helps businesses scale their AI initiatives and continuously optimize their models while maintaining high standards of performance, quality, and efficiency.

Quality and Trust

A core advantage of AWS’s framework is its focus on maintaining quality and trust in AI deployments. By integrating responsible AI metrics such as accuracy, fairness, and safety, the system ensures that models meet high ethical standards. Automated evaluation, combined with human-in-the-loop validation, helps businesses monitor their LLMs for reliability, relevance, and safety. This comprehensive approach to evaluation ensures that LLMs can be trusted to deliver accurate and ethical outputs, building confidence among users and stakeholders.

Successful Real-World Applications

Amazon Q Business

AWS’s evaluation framework has been applied to Amazon Q Business, a managed Retrieval Augmented Generation (RAG) solution. The framework supports both lightweight and comprehensive evaluation workflows, combining automated metrics with human validation to optimize the model’s accuracy and relevance continuously. This approach enhances business decision-making by providing more reliable insights, contributing to operational efficiency within enterprise environments.

Bedrock Knowledge Bases

In Bedrock Knowledge Bases, AWS integrated its evaluation framework to assess and improve the performance of knowledge-driven LLM applications. The framework enables efficient handling of complex queries, ensuring that generated insights are relevant and accurate. This leads to higher-quality outputs and ensures the application of LLMs in knowledge management systems can consistently deliver valuable and reliable results.

The Bottom Line

AWS’s Automated Evaluation Framework is a valuable tool for enhancing the performance, reliability, and ethical standards of LLMs. By automating the evaluation process, it helps businesses reduce time and costs while ensuring models are accurate, safe, and fair. The framework’s scalability and flexibility make it suitable for both small and large-scale projects, effectively integrating into existing AI workflows.

With comprehensive metrics, including responsible AI measures, AWS ensures LLMs meet high ethical and performance standards. Real-world applications, like Amazon Q Business and Bedrock Knowledge Bases, show its practical benefits. Overall, AWS’s framework enables businesses to optimize and scale their AI systems confidently, setting a new standard for generative AI evaluations.

[faisalakhtar12]

The Serverless Development Dilemma: Local Testing in a Cloud-Native World

Picture this: You’re an AWS developer, sitting in your favorite coffee shop, sipping on your third espresso of the day. You’re working on a cutting-edge serverless application that’s going to revolutionize… well, something. But as you try to test your latest feature, you realize you’re caught in a classic “cloud” vs “localhost” conundrum. Welcome to the serverless development dilemma! The…

[pumoxi]

Implementing API Gateway with Lambda Authorizer Using Terraform

Implementing a secure and scalable API Gateway with Lambda authorizer. Leverage Terraform to manage your resources efficiently.

Background: API Gateway with Lambda AuthorizerBenefits of Using API Gateway with Lambda AuthorizerOverview of the Terraform ImplementationDetailed Explanation of the Terraform CodeProviderVariableLocalsData SourcesIAM Roles and PoliciesIAM Role for Core FunctionIAM Role for Lambda Authorizer FunctionLambda Core FunctionLambda Authorizer FunctionBenefits of Using Environment VariablesAPI Gateway…