Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
Fetch, a company that uses machine learning to optimize supply chain logistics, has achieved a 50% reduction in ML processing latency using Amazon SageMaker and Hugging Face's Transformers library. The team used the library's pre-trained models to fine-tune their own models on specific tasks, resulting in faster inference times. According to Fetch, this improvement enables them to make more accurate predictions and respond quickly to changing market conditions.
Fetch, a company that uses machine learning to optimize supply chain logistics, has achieved a 50% reduction in ML processing latency using Amazon SageMaker and Hugging Face's Transformers library. The team used the library's pre-trained models to fine-tune their own models on specific tasks, resulting in faster inference times. According to Fetch, this improvement enables them to make more accurate predictions and respond quickly to changing market conditions.
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Why it matters: This matters because reducing latency in ML processing can have a significant impact on real-world applications, such as supply chain optimization, where timely decisions can lead to cost savings and improved efficiency.
Source: https://huggingface.co/blog/fetch-case-study
This article was originally published at: https://huggingface.co/blog/fetch-case-study