AI BriefingFetch Cuts ML Latency by 50% with SageMaker
Key point
Fetch optimized its ML pipeline using SageMaker and Hugging Face, reducing latency by 50% and increasing accuracy by 200%.
Details
Fetch, which processes over 80 million receipts weekly, optimized its ML pipeline using Amazon SageMaker and Hugging Face. The key challenge was improving the speed and accuracy of extracting text from receipts and structuring the data.
By using the Hugging Face AWS Deep Learning Container, they improved model quality, and by leveraging Amazon SageMaker's Model Training and Processing capabilities, they managed large-scale workloads. They also adopted multi-GPU instances to maximize inference and runtime performance.
Key achievements include:
- 50% reduction in ML processing latency
- 200% improvement in document understanding model accuracy
- Improved model deployment and scalability: Increased operational efficiency and development productivity through standardized deployment processes
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