Looper: An end-to-end ML platform for product decisions

TL;DR

Looper is an end-to-end machine learning platform designed for product decisions, enabling non-ML engineers to deploy and evaluate models aligned with product goals, supporting personalization, causal evaluation, and Bayesian tuning.

Contribution

It introduces a comprehensive architecture and APIs for an ML platform that simplifies deployment, evaluation, and optimization of models for product teams.

Findings

Hosted 440-1,000 models during deployment

Made 4-6 million real-time decisions per second

Supported personalization and causal evaluation

Abstract

Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support finegrain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection. Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals. During the 2021 production deployment Looper simultaneously hosted 440-1,000 ML models that made 4-6 million real-time decisions per second. We sum up experiences of platform adopters and describe their learning curve.