arXiv Machine Learning July 2, 2026 By Shahbaz Alvi, Giusy Fedele, Gabriele Accarino, Italo Epicoco, Ilenia Manco, Pasquale Schiano

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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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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