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.
Call for Papers: Advancing the Frontiers of Engineering and Optimisation for AI Systems at Scale
AISystems​ is an international scholarly journal aiming to be a central hub for knowledge exchange in the field of AI systems engineering and optimisation at scale. As AI models grow increasingly complex and integral across industries, the challenges in their system design, deployment, maintenance, and governance have become critical. This journal addresses this need by focusing on bridging the gap between cutting-edge model research and the industrial deployment of reliable systems. The journal scope includes, but is not limited to: • AI System Engineering: Including architecture design, deployment strategies, and performance optimisation. • Large-scale & Multimodal Systems: Development, evaluation, and application. • System Robustness: Testing, benchmarking, validation, and failure analysis. • MLOps & Lifecycle Management: CI/CD, model governance. • Trustworthy AI: Transparency, fairness, accountability, and bias mitigation. • Edge AI & Distributed Inference: Embedded intelligence and distributed frameworks. • Autonomous Agents & Automation: Multi-agent systems and automated pipelines. • Hardware Acceleration & Green Computing: GPU/TPU optimisation, energy-efficient AI, and compute infrastructure. Target Audience & Contributors:​ We welcome submissions from academics, researchers in industrial labs, as well as frontline AI system engineers, architects, and product leads. Your contributions will help steer the field of AI systems towards greater efficiency, reliability, and sustainability. Submit your research and help define the future of AI systems with us. For submission guidelines and further information, please visit the journal's official website.
Join Us in Shaping the Future: AISystems Seeks Editorial Board Members
The AISystems journal is committed to publishing high-quality research on the design, deployment, evaluation, and automation of AI systems, with a special emphasis on scalability, robustness, and accountability. As AI technologies permeate all sectors, building reliable, efficient, and ethical AI systems has become a global imperative. We warmly invite active researchers, engineers, and thought leaders in the following and related areas to join our Editorial Board: AI Systems Engineering, MLOps, and Large-scale Model Deployment Development and Evaluation of Large Language Models, Multimodal Systems, and Agent Systems Trustworthy AI: Fairness, Transparency, Accountability, and Governance Edge AI, Hardware Acceleration, and Green, Efficient AI Computing Autonomous Systems, Automation Pipelines, and AI-Enabled Robotics As an Editorial Board Member, you will have the opportunity to: Guide the academic direction of the field by reviewing cutting-edge submissions. Participate in special issue planning and organize academic events to enhance the journal's impact. Build deep connections within the global AI systems community. Contribute crucially to shaping a responsible and scalable AI future. We look forward to welcoming you, with your passion, professionalism, and global vision, to our distinguished team. For application details and more information, please contact: editorial-ais@luminapress.co.uk
AISystems Launches: Advancing Scalable, Reliable, and Sustainable AI Systems Engineering
We are pleased to announce the launch of AISystems. As AI technologies become deeply embedded across industries, building reliable, efficient, and governable AI systems has emerged as a critical challenge for both academia and industry. AISystems is created to bridge theoretical AI research and systems practice, advancing the real-world deployment of AI at scale. Topics covered include, but are not limited to: AI systems engineering, including design, deployment, and optimization strategies Development and evaluation of large language models, foundation models, and multimodal AI systems Robustness testing, benchmarking, model validation, and failure analysis MLOps, CI/CD, model governance, and lifecycle management Trustworthy AI practices: 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/TPU optimization, energy-efficient AI, and green computing infrastructure We welcome submissions from academic, industrial, and interdisciplinary teams to jointly drive innovation in AI system performance, reliability, and sustainability.