Reference no: EM133883390 , Length: word count:1500

Cloud Computing

Title: Implementing an Image Object Identification System on AWS Using YOLOv5, Lambda, API Gateway, and DynamoDB

Introduction
The objective of this assignment is to design and implement an image object identification system on AWS. The system will leverage YOLOv5, a state-of-the-art object detection model, to identify objects within images. The identified objects' data will then be stored in a DynamoDB table using AWS

Lambda and API Gateway. This project will demonstrate the integration of machine learning models with serverless architecture, enabling scalable and efficient image processing in the cloud. Get online assignment help services Now!

Tasks to be Accomplished
Project Setup and Environment Configuration

Task 1.1: Setting Up AWS Environment o Create an AWS account (if not already existing) and configure the necessary IAM roles and permissions.

Set up an S3 bucket to store the images for object detection.

Prepare the environment for deploying YOLOv5, which may include setting up an EC2 instance.

Task 1.2: Installing and Configuring YOLOv5 o Download and set up the YOLOv5 model on the chosen environment (EC2).
Ensure all dependencies such as Python, PyTorch, and other libraries required by YOLOv5 are installed and configured.
Verify the YOLOv5 setup by running a sample image through the model and confirming that the object detection works correctly.

Developing the Lambda Function

Task 2.1: Writing the Lambda Function Code
Develop an AWS Lambda function that will trigger YOLOv5 to perform object detection on images stored in the S3 bucket.
The function should read the image from S3, run the YOLOv5 model, and extract the identified objects.
Ensure identified objects are stored in S3 bucket.
Ensure the Lambda function is stateless and adheres to best practices for serverless functions, including error handling and logging.

Task 2.2: Testing the Lambda Function o Create a sample image dataset in the S3 bucket to test the Lambda function.
Validate that the Lambda function correctly processes the images, extracts object information.
Debug and resolve any issues identified during testing.
Setting Up API Gateway

Task 3.1: Configuring API Gateway o Set up an AWS API Gateway that will serve as the front-end for the Lambda function. o Configure the API Gateway to accept HTTP requests, triggering the Lambda function with the required parameters (e.g., S3 bucket location of the image).
Implement necessary security measures, such as API keys or IAM roles, to control access to the API.

Task 3.2: Integrating API Gateway with Lambda o Test the integration by sending HTTP requests through the API Gateway and verifying that they trigger the Lambda function.
Ensure that the API Gateway can handle concurrent requests and properly forward responses back to the client.
Storing Data in DynamoDB

Task 4.1: Designing the DynamoDB Table o Design a DynamoDB table schema to store the results of the object detection.
The table should include fields such as ImageID, DetectedObjects, Timestamp, and any other relevant metadata.

Task 4.2: Writing to DynamoDB o Modify the Lambda function to write the detected objects' data into the DynamoDB table.
Ensure the function handles cases where the write operation fails, including implementing retries or logging errors.

Task 4.3: Validating Data Storage o Test the entire pipeline by uploading a new
image to the S3 bucket, triggering the API Gateway, running the Lambda function, and verifying the data stored in DynamoDB.
Perform query operations on DynamoDB to ensure the data is stored correctly and is retrievable.
Optimizing and Scaling the Solution

Task 5.1: Performance Optimization o Analyze the performance of the Lambda function, focusing on execution time and resource consumption.
Implement optimizations such as increasing memory allocation or reducing cold start times.

Task 5.2: Scaling Considerations
Evaluate the system's scalability by simulating a high volume of image uploads and API requests.
Implement auto-scaling policies for Lambda and DynamoDB to handle increased loads.
Documentation and Final Report

Task 6.1: Writing the Technical Documentation o Prepare detailed documentation of the project, including the architecture, code, and configuration settings.
Provide instructions for deploying and running the system, as well as troubleshooting tips.

Task 6.2: Preparing the Final Report o Write a comprehensive report summarizing the project, including the design decisions, challenges encountered, and how they were resolved.
Include screenshots, diagrams, and code snippets to illustrate key aspects of the implementation.

Task 6.3: Recorded PowerPoint Presentation
Prepare a 5 minutes PowerPoint recorded presentation to defend the originality of your tasks.
Include your project introduction, your methodology, output & discussion on results, conclusion, and other relevant aspects (if any) in your presentation.

Conclusion
This assignment will provide hands-on experience in building a serverless application on AWS that integrates machine learning with cloud services. By the end of this project, students will have developed a deep understanding of how to deploy machine learning models in the cloud, manage
serverless functions, and utilize AWS services like API Gateway and DynamoDB to build scalable and efficient solutions.