Video Content Moderation with SageMaker Pipelines and Open-Source Models
I have built video analysis pipelines that process thousands of uploads per day, routing each file through multiple ML models for content moderation, face recognition, transcription, and object detection. The architecture I keep returning to uses SageMaker Pipelines as the orchestration backbone, with open-source models deployed across Processing Jobs and Batch Transform steps. This approach gives you full control over model versions, GPU instance selection, and inference logic without per-API-call pricing from managed AI services. The tradeoff is real: you own every container, every model artifact, and every failure mode. This article is the architecture reference for building that pipeline. I cover model selection for each analysis domain, the SageMaker Pipeline DAG design, GPU instance sizing, and the operational patterns that keep it running at scale. If you need a deeper understanding of how SageMaker Pipelines work under the hood, start with SageMaker Pipelines: An Architecture Deep-Dive.