Project

AI Powered Image Analysis Application

Easy

32 completions

~ 5 hours

4.5

This project will help you gain hands-on experience with AWS cloud services, including S3, Rekognition, Lambda, and SQS. You'll learn how to set up and secure cloud resources, integrate AI-powered image recognition, and implement a web interface to interact with these services. By the end of this project, you'll have a comprehensive understanding of building decoupled, highly scalable, and secure cloud applications.

Provided by

JetBrains Academy

About

Step into the exciting world of cloud computing and artificial intelligence with this hands-on project. You'll build an image analysis component that verifies if photos uploaded to a food sales website are vegetables using Amazon Rekognition. You'll work with various AWS services such as IAM, S3, Rekognition, Lambda, and SQS message queues, thus enhancing your mastery of cloud computing.

Graduate project

This project covers the core topics of the DevOps Engineer with AI: CI/CD Pipelines & Docker Skills course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Start by setting up an AWS account and creating an IAM user, role, and policies that grant the necessary permissions for your application. This ensures that your application and Lambda function can securely interact with various AWS services.

Create an S3 bucket to store the images uploaded by your application. Set the necessary bucket policies to control access.

Integrate AWS Rekognition to analyze images. Send sample images to Rekognition to detect labels and understand the kind of response it returns. Explore other capabilities of Amazon Rekognition, such as facial analysis, text detection, and celebrity detection.

Create a Lambda function that is triggered when images are uploaded to the S3 bucket. It will send the images to Rekognition for analysis and format the JSON response for use by the application. View analysis results in CloudWatch logs.

Create an SQS queue to hold the analysis results from the Lambda function. Modify the Lambda function to send analysis results to the queue. Integrate the provided web app in this workflow to send images to S3, read messages from the queue, and display the results.

Reviews

Zezva Okhanashvili

7 months ago

5.0

I have learned how to use AWS different services and policies, how to attach policies to roles and users

Marcin Borkowski

7 months ago

4.7

Nice introduction to AWS, project is clear and easy (maybe too easy). But still clearly conduct you through basics of AWS where learning curve is a tragedy.

Samuel Zuniga

8 months ago

4.0

I think this is a really cool and fun way to learn about AWS components. I did have a lot of difficulty getting the data for the main.py from the code using the CL likely because I was just using the windows command line with AWS CL installed on top.

4.5

Learners who completed this project within the DevOps Engineer with AI: CI/CD Pipelines & Docker Skills course rated it as follows:

Usefulness

4.7

Fun

4.6

Clarity

4.1