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Level Up Your Auto-Tagging Pipeline on AWS
- The article discusses how to level up an auto-tagging pipeline on AWS using Amazon Rekognition, AWS Lambda, and S3.
- It covers steps like uploading a test image to S3, viewing results in CloudWatch Logs, and saving tags in a JSON file in S3.
- After setting up the basics with an S3 Bucket, Lambda Function, and Rekognition Pipeline, the process involves uploading a sample image for detection.
- The choice of image extension (.jpg, .jpeg, or .png) is crucial, and uploading is done via the AWS Console into the designated S3 bucket.
- Upon upload, Lambda function processes the image by sending it to Amazon Rekognition for object and scene detection, while logging details in CloudWatch.
- CloudWatch Logs provide insight into the detected labels and their confidence percentages, with a structured output format.
- Detected labels are then saved as a JSON file in the S3 bucket's 'tags/' directory, following a specific folder structure.
- The Lambda code is updated to detect labels, save them as JSON, and the deployment process involves testing with a new image upload.
- The JSON file contains label details like name and confidence percentage, accessible for viewing and download.
- The article concludes by encouraging further exploration, suggesting bonus features like email notifications via SNS and code modularization.
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