TechOps Examples
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🧠 USE CASE
AWS ML Architecture for Image and Video Analysis
Have you ever wondered how Netflix recommends the perfect thumbnail, how Facebook automatically tags friends in photos, or how Instagram detects explicit content in photos?
Use cases like these can be built on event driven ML architectures with services like AWS Rekognition for image and video analysis.
Here is a sample serverless event driven pipeline designed to analyze uploaded media in real time.
1. User Upload via API Gateway
The pipeline starts when a user uploads an image/video from a frontend application.
This upload request is sent to an Amazon API Gateway endpoint.
API Gateway acts as a managed entry point, validating and routing requests to AWS Lambda.
2. Lambda Triggers Rekognition
Lambda receives the image/video data and metadata, then calls AWS Rekognition for analysis
3. AWS Rekognition Analysis
The Lambda function invokes AWS Rekognition APIs to perform image/video analysis.
Based on the use case, Rekognition applies multiple ML models, such as:
Object Detection – Identifies objects and their bounding box coordinates.
Scene Detection – Categorizes the image (e.g., beach, forest, cityscape).
Facial Analysis – Extracts age, gender, and emotion insights.
Content Moderation – Flags inappropriate or unsafe content.
Celebrity Recognition – Matches faces against a celebrity database.
Smart Image Tagging – Organizes objects into metadata categories.
4. Lambda Decides What to Do with the Image/Video
Based on the AWS Rekognition response, the Lambda function determines whether to store the image/video.
Two possible outcomes:
[a] If the content is appropriate:
The Lambda function uploads the media to an Amazon S3 bucket for persistent storage.
It then returns a success response via API Gateway, confirming the upload.
[b] If the content is inappropriate:
The Lambda function does NOT store the media in S3.
It returns a violation response via API Gateway, notifying the user about content restrictions.
Design Considerations for Scalability & Performance
[1] For large workloads, SQS can decouple requests and allow Lambda to process them asynchronously.
[2] Step Functions can orchestrate multi step processing workflows (e.g., Rekognition → Comprehend → Notification).
[3] Use S3 lifecycle policies to delete media after a certain period if it’s no longer needed.
[4] Restrict API Gateway endpoints with IAM policies, JWT authentication, or Cognito.
[5] Apply IAM least privilege to Lambda and S3 to prevent unnecessary access.
[6] Enable logging & monitoring with CloudWatch for auditing purposes.
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