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Optimizing AI development efficiency through MLOps and security integration: A measurement approach using data envelopment analysis (DEA)
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DOI：10.1016/j.future.2026.108503.png)
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Abstract
En 中文
The growing use of artificial intelligence (AI) in engineering industries is exacerbating the trade-off between speed of development and security. Although MLOps help with automation and scaling, they are often perceived as introducing restrictions to workflow that slow down AI efforts. There is hardly any quantitative evidence regarding the impact of security integration on the development efficiency.
Keywords:
AI development efficiency
MLOps
security integration
data envelopment analysis
engineering industries
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