AWS Rekognition Pipeline
Processed 50,000+ images at 99.9% uptime with fully serverless event-driven architecture
50,000+ images
99.9% uptime
Fully serverless
Overview
Event-driven serverless pipeline processing facial expressions at scale. Leveraged AWS Rekognition for ML analysis, automated data workflows with Lambda, and visualized insights through QuickSight.
Architecture Diagram
Architecture diagram coming soon.
See the GitHub repository for architecture documentation.
Design Decisions
- →Chose event-driven architecture (S3 → Lambda → Rekognition) to avoid polling and reduce cost.
- →Used Athena for querying results instead of a traditional database no server to manage, pay per query.
- →QuickSight for visualization because it integrates natively with Athena and S3 without ETL pipelines.
- →Lambda concurrency limits were configured to prevent runaway costs during high-volume processing.
Deployment
Fully serverless no servers to manage. Lambda functions deployed via AWS SAM. S3 bucket configured with event notifications to trigger the pipeline. IAM roles follow least-privilege principle throughout.
Lessons Learned
Event-driven architectures are powerful but require careful error handling. A failed Lambda invocation can silently drop events without a dead-letter queue. Adding DLQ and CloudWatch alarms was the most important reliability improvement made after initial deployment.