
Content moderation pipeline and recommendation engine
Challenge
A client building a mobile media platform needed a content moderation pipeline and a custom recommendation engine that learned from user interactions.
Solution
- I built a Kinesis-to-S3 signal pipeline with Glue ETL for interaction data
- I orchestrated content moderation with Step Functions (Rekognition, Transcribe)
- I trained a SageMaker recommendation model on an OpenSearch vector database
- I provisioned all of it with AWS CDK
Outcome
Processing time fell from 4–6 minutes to under 30 seconds once the moderation steps ran in parallel.











