About the Journal

AISystems aims to advance the engineering and optimisation of artificial intelligence systems at scale. The journal focuses on the full lifecycle of AI model design, deployment, evaluation, and automation emphasising system robustness, efficiency, and accountability. It supports research spanning LLMs, multimodal models, MLOps tooling, edge computation, agent systems, and AI-enabled robotics. AISystems prioritises practical, scalable solutions that address real-world challenges in computation, operations, reliability, and governance. The dedicated sustainability scope strengthens its relevance by promoting energy-efficient AI and resource-conscious algorithm design. 

This journal covers the following topics, but not limited to: 

• AI system engineering including design, deployment and optimisation strategies.
• Development and evaluation of large language models, foundation models and multimodal AI systems.
• Robustness testing, benchmarking, model validation and failure analysis.
• MLOps, continuous integration and continuous deployment, model governance and lifecycle management.
• Trustworthy AI practices including transparency, fairness, accountability and bias mitigation.
• Edge AI, embedded intelligence and distributed inference frameworks.
• Autonomous agents, multi agent systems and intelligent automation pipelines.
• Hardware acceleration, GPU and TPU optimisation, energy efficient AI and green compute infrastructures.