Towards blended modeling and simulation of DevOps processes

Colantoni, Alessandro; Berardinelli, Luca; Garmendía, Antonio; Bräuer, Johannes

doi:10.1145/3550356.3561597

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Model-driven Engineering Of Mlops or Devops1

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2023

2024

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“…It involves various components such as ML data sources and datasets, repositories of ML models and metadata, automated ML pipelines, and workflow. The application of model-driven engineering in MLOps or De-vOps is still early, despite the extensive research conducted in MLOps within a centralized setting, mainly driven by the widespread adoption of cloudbased MLOps services [25].…”

Section: Model-driven Engineering Of Mlops or Devopsmentioning

confidence: 99%

Decentralized Machine Learning Governance: Overview, Opportunities, and Challenges

Alsagheer,

Xu,

Shi

2023

IEEE Access

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Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology.In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.

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Section: Model-driven Engineering Of Mlops or Devopsmentioning

confidence: 99%