Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face

This article is a cross-post from an originally published post on September 2023 on AWS's website.

Overview

Consumer engagement and rewards company Fetch offers an application that lets users earn rewards on their purchases by scanning their receipts. The company also parses these receipts to generate insights into consumer behavior and provides those insights to brand partners. As weekly scans rapidly grew, Fetch needed to improve its speed and precision.

On Amazon Web Services (AWS), Fetch optimized its machine learning (ML) pipeline using Hugging Face and Amazon SageMaker , a service for building, training, and deploying ML models with fully managed infrastructure, tools, and workflows. Now, the Fetch app can process scans faster and with significantly higher accuracy.

Opportunity | Using Amazon SageMaker to Accelerate an ML Pipeline in 12 Months for Fetch

Using the Fetch app, customers can scan receipts, receive points, and redeem those points for gift cards. To reward users for receipt scans instantaneously, Fetch needed to be able to capture text from a receipt, extract the pertinent data, and structure it so that the rest of its system can process and analyze it. With over 80 million receipts processed per week—hundreds of receipts per second at peak traffic—it needed to perform this process quickly, accurately, and at scale.

In 2021, Fetch set out to optimize its app’s scanning functionality. Fetch is an AWS-native company, and its ML operations team was already using Amazon SageMaker for many of its models. This made the decision to enhance its ML pipeline by migrating its models to Amazon SageMaker a straightforward one.

Throughout the project, Fetch had weekly calls with the AWS team and received support from a subject matter expert whom AWS paired with Fetch. The company built, trained, and deployed more than five ML models using Amazon SageMaker in 12 months. In late 2022, Fetch rolled out its updated mobile app and new ML pipeline.