Interviews in a semiconductor company reveal 16 collaboration and communication challenges in ML engineering teams, with unclear roles and responsibilities as the top issue, and list effective mitigation practices under hardware-driven constraints.
Machine learning ex- plainability for external stakeholders
2 Pith papers cite this work, alongside 40 external citations. Polarity classification is still indexing.
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An architecture that decouples explanation generation from model inference, using semantic caching, lightweight verification, and adaptive method selection, claims 38% lower latency and 3.2x throughput on edge AI deployments.
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Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry
Interviews in a semiconductor company reveal 16 collaboration and communication challenges in ML engineering teams, with unclear roles and responsibilities as the top issue, and list effective mitigation practices under hardware-driven constraints.
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Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems
An architecture that decouples explanation generation from model inference, using semantic caching, lightweight verification, and adaptive method selection, claims 38% lower latency and 3.2x throughput on edge AI deployments.