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arXiv Machine Learning By Shahbaz Alvi, Giusy Fedele, Gabriele Accarino, Italo Epicoco, Ilenia Manco, Pasquale Schiano

OpFML: Pipeline for ML-based Operational Inference

Read the original on arXiv Machine Learning →

arXiv:2601. 11046v2 Announce Type: replace Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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