Fetch 採用 Amazon SageMaker 與 Hugging Face，成功降低 50% 機器學習處理延遲
Original: Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
This case study examines how Fetch, a leading consumer rewards platform in the United States, leveraged the collaboration between Amazon…
美國知名消費回饋平台 Fetch 每日需處理數百萬張發票收據。為了提升 OCR、商品匹配與商家分類等 NLP 任務的效率，Fetch 採用了 Amazon SageMaker 與 Hugging Face 的整合方案。此舉不僅讓機器學習模型的推理延遲大幅降低 50%，同時也優化了運算成本與部署流程。
This case study examines how Fetch, a leading consumer rewards platform in the United States, leveraged the collaboration between Amazon SageMaker and Hugging Face to overcome performance bottlenecks in large-scale machine learning (ML) inference.
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