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A Synthesis of Green Architectural Tactics for ML-Enabled Systems
The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associated with designing environmentally friendly ML-enabled systems.
Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems
However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge.
Priority Quality Attributes for Engineering AI-enabled Systems
We assert that the quality attributes of security, privacy, data centricity, sustainability, and explainability pose new challenges to AI engineering and will drive the success of AI-enabled systems in the public sector.
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