Fetch Cuts reduces ML processing latency by 50% via Amazon SageMaker & Hugging Face

AI Impact Summary

Fetch migrated its ML pipeline to Amazon SageMaker and Hugging Face containers, leveraging SageMaker Training, Processing, and Inference Recommender to accelerate model training, tuning, and production deployment. The implementation, combined with Hugging Face Inference Toolkit and AWS DL Containers, enabled a 50% reduction in latency for the slowest receipt scans and support for multi-GPU inference at scale. The shift toward SageMaker Shadow Testing and automated deployment reduces manual toil and accelerates iteration across models, increasing reliability as transaction volume grows.