How Meituan Built a One‑Stop Machine Learning Platform for Delivery Optimization
This article explains how Meituan’s delivery business has transitioned from data online to AI‑driven decision making by building a comprehensive, one‑stop machine learning platform that includes model management, data graph, feature store, AB testing, and a machine‑learning definition language to accelerate algorithm iteration and reduce operational costs.
Internet development has shifted from data online to process automation, and now we are in the era of AI‑driven decision making, where machine learning and AI algorithms become increasingly important.
Delivery is a crucial link in the closed‑loop of food‑delivery platforms; massive operational data collected offline provides a solid foundation for applying machine learning to improve efficiency and reduce operational costs. Algorithms already appear in many delivery services such as order dispatch, pricing, and time prediction.
To boost machine‑learning development efficiency, clarify the boundary between algorithms and engineering, and support rapid iteration, a one‑stop machine learning platform was built on top of existing tools, adopting a platform‑centric, data‑closed‑loop approach.
Model Management Platform: Provides a unified model format, supporting model discovery, deployment, and switching, and accommodates partitioned and ultra‑large models.
Data Graph: Builds multi‑dimensional metadata indexes on top of data layers, solving the problem of data discovery for algorithm engineers.
Feature Platform: Offers a unified offline feature production pipeline and a stable, high‑throughput online feature retrieval interface, supporting high concurrency scenarios.
AB Experiment Platform: Supplies traffic splitting, event logging, and automatic evaluation of experiment results for online experiments.
Machine Learning Definition Language (MLDL): Defines the entire machine‑learning workflow—including data cleaning, extraction, training‑set construction, model training, evaluation, and online prediction.
The presentation outline includes: 1) Delivery business overview; 2) Applications of machine learning in Meituan delivery; 3) Meituan delivery algorithm data platform; 4) Future evolution and outlook.
Key benefits focus on how the full machine‑learning process can be realized in practice and how to efficiently support rapid algorithm iteration.
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