To delight customers and minimize packaging waste, Amazon must select the optimal packaging type for billions of packages shipped every year. If too little protection is used for a fragile item such as a coffee mug, the item will arrive damaged and Amazon risks their customer’s trust. Using too much protection will result in increased costs and overfull recycling bins. With hundreds of millions of products available, a scalable decision mechanism is needed to continuously learn from product testing and customer feedback.
To solve these problems, the Amazon Packaging Innovation team developed machine learning (ML) models that classify whether products are suitable for Amazon packaging types such as mailers, bags, or boxes, or could even be shipped with no additional packaging. Previously, the team developed a custom pipeline based on AWS Step Functions to perform weekly training and daily or monthly inference jobs. However, over time the pipeline didn’t provide

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