Entity Prediction Service: a configurable, end-to-end AutoML system

Business teams at Amazon often need to classify products into different taxonomies such as GL, item type keyword (ITK), category/sub-category, browse node, tax code, export-compliance-code and hazmat. Due to the lack of ML expertise, these teams end up relying on human auditors or manually codify rules, which is not scalable or do not work in cases having data with high diversity. Existing ML solutions for AutoML product classification are either stand-alone applications that push the burden of model productionization onto users (e.g. SageMaker Autopilot and AutoGluon), or are production-friendly, but lack state-of-the-art AutoML capabilities and do not leverage the agility offered by modern tech ecosystems. In this paper, we present Entity Prediction Service (EPS), a configurable product classification solution designed to serve the end-to-end needs of Amazon teams. Leveraging the robust ecosystem of AWS services and Docker, EPS automatically fetches and pre-processes data from internal data sources, trains and tunes models, performs inference, and enables one-click deployment into production. Each step offers a granular level of configurability, with default parameters backed by a robust set of scientific benchmarks. This helps serve customers across the spectrum of Machine Learning expertise, enabling Business Associates, SDEs and Applied Scientists to build high quality product classification models and deploy them on Amazon systems for continuous classification.

Citation: Gaurav Manchanda, Abhishek Divekar, Akshay Jagatap, Prit Raj, Vinayak Puranik, Nikhil Rasiwasia, Ramakrishna Nalam, and Jagannathan Srinivasa. “Entity Prediction Service: a configurable, end-to-end AutoML system”. Workshop on Automated Machine Learning at the 8th conference of Amazon Machine Learning (AMLC 2020) (Poster)