Looper: An end-to-end ML platform for product decisions
This addresses the problem of enabling product engineers to use ML for data-driven decisions, though it appears incremental as it builds on prior platform shortcomings.
The paper tackled the challenge of building an end-to-end ML platform for product decisions, introducing Looper which accommodated non-ML engineers and supported features like causal evaluation, and it was deployed in production handling 4-6 million decisions per second with 440-1,000 models.
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.