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Paper reviews DataOps and MLOps for trustworthy AI in transportation

By PulseAugur Editorial · [1 sources] ·

This paper reviews the critical role of DataOps and MLOps in advancing Intelligent Transportation Systems and Logistics (ITS&L). It addresses current literature gaps and explores the components, tools, and practical applications of these operational frameworks within the ITS&L domain. The research also emphasizes the importance of trustworthiness in AI systems for real-world ITS&L scenarios, discussing methods to enhance confidence and outlining future challenges and prospects. AI

IMPACT Establishes a foundational understanding for developing more efficient and trustworthy intelligent transportation and logistics systems.

RANK_REASON The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper reviews DataOps and MLOps for trustworthy AI in transportation

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16 / 100
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The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra, product
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Antonio Emanuele Cin\`a, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto ·

    Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

    arXiv:2610.01282v1 Announce Type: new Abstract: The rapid evolution of Intelligent Transportation Systems and Logistics (ITS\&amp;L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specific…

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