将机器学习模型部署到气候业务系统，一站式解决数据获取与异常处理问题。
OpFML: Pipeline for ML-based Operational Inference
- 用TOML配置文件整合数据摄入、异常处理、预处理和推理四步流程
- 在意大利南部每日火灾活动预测中验证，减少重复开发代码量
- 适合需要快速部署模型的气象与地球科学业务系统
用于气候与地球科学的机器学习模型能力日益增强，但其在业务系统中的部署仍面临重大挑战：通用模型服务工具（如MLflow和KServe）假设推理节点已具备输入数据，而数据获取、异常处理与预处理则依赖独立工作流。本文提出OpFML：基于机器学习的业务推断流程——一个可配置的集成式工作流，将数据摄入、异常应对、预处理和模型推理四个步骤统一于单一TOML配置中。通过整合这些环节，OpFML显著减少了每次新部署所需的冗余代码。我们在意大利南部每日火灾活动预测任务上展示了该流程的有效性。
原文摘要 · Abstract (English)
Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-purpose model-serving tools, such as MLflow and KServe, assume input data availability at the inference node, while data acquisition, failure handling, and preprocessing are trusted to a separate workflow. We present OpFML: Operational Forecasting with Machine Learning - a configurable pipeline integrating the four steps of operational inference into a single TOML-configured workflow: data consumption, contingency handling, preprocessing, and model inference. By consolidating these steps, OpFML removes the significant boilerplate code required for each new deployment. We demonstrate the pipeline on the operational forecasting of daily fire activity over southern Italy.
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