The International Journal of eScience

Computing infrastructures and systems are rapidly developing and so are novel ways to map, control and execute scientific applications which become more and more complex and collaborative.
Computational and storage capabilities, databases, sensors, and people need true collaborative tools. Over the last years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed sensing and computing systems. Big Data in all its guises require novel methods and infrastructures to register, analyze and distill meaning.

FGCS aims to lead the way in advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT).

The Aims and Scope of FGCS cover new developments in:

[1] Applications and application support:

    Novel applications for novel e-infrastructures
    Complex workflow applications
    Big Data registration, processing and analyses
    Problem solving environments and virtual laboratories
    Semantic and knowledge based systems
    Collaborative infrastructures and virtual organizations
    Methods for high performance and high throughput computing
    Urgent computing
    Scientific, industrial, social and educational implications
    Education

[2] Methods and tools:

    Tools for infrastructure development and monitoring
    Distributed dynamic resource management and scheduling
    Information management
    Protocols and emerging standards
    Methods and tools for internet computing
    Security aspects

[3] Theory:

    Process specification;
    Program and algorithm design
    Theoretical aspects of large scale communication and computation
    Scaling and performance theory
    Protocols and their verification

Special Issue on MLOps Advancements: Improving Development, Management, and Interpretability in AI and Machine Learning
Día de Entrega: 2026-03-31

Motivation and Scope 

The rapid advancement of data-driven artificial intelligence has accelerated the integration of machine learning (ML) models into real-world applications across diverse domains. This transition from experimental ML to production-ready AI systems has unveiled significant operational challenges in model development, deployment, monitoring, and maintenance. MLOps has emerged as a critical framework of practices and tools designed to streamline the entire ML lifecycle, ensuring automation, reproducibility, and scalability while bridging the gap between data science experimentation and the reliable operation of ML components in production. 

As AI adoption continues to expand, the need for robust, scalable, and transparent MLOps practices has become increasingly vital to guarantee the reliability and trustworthiness of deployed ML models. This Special Issue seeks to gather cutting-edge research, case studies, and insights from both academia and industry that address the key challenges and emerging trends in MLOps, with particular emphasis on practical solutions for improving AI and machine learning workflows. 

We invite high-quality submissions that explore, but are not limited to, the following topics: 

    MLOps Frameworks and Best Practices 
    ML pipelines orchestration 
    ML model reproducibility, traceability, and explainability
    Continuous integration/continuous delivery (CI/CD) practices for ML models
    ML model monitoring and observability
    MLOps practices to ensure Interpretability and Explainability in AI systems
    Application of MLOps principles to Knowledge and Semantic Representation
    Application of MLOps principles to large language models (LLMOps)
    ML-specific architecture design and patterns
    Experience reports on real-world MLOps applications
    Challenges in applying MLOps to specific domains (e.g., healthcare and finance) 
    Ethics and Accountability in MLOps
    AutoML applications in MLOps
    Collaboration and team dynamics in MLOps
    Regulatory and policy aspects of MLOps
    MLOps strategies for Green AI
    Security and data privacy in MLOps

The proponents of this Special Issue are also the organizers of the Workshop on Machine Learning Operations – MLOps'25, which will be held at ECAI 2025, one of the premier conferences in artificial intelligence. As part of this initiative, we aim to provide an opportunity for authors of papers accepted at the workshop to submit an extended version of their work to this Special Issue. This will enable further development of innovative research contributions presented at MLOps'25, fostering deeper discussions and broader dissemination of advancements in MLOps methodologies, tools, and applications.

Guest Editors

Antonella Carbonaro
University of Bologna, Bologna, Italy
antonella.carbonaro@unibo.it 

Luigi Quaranta
University of Bari, Bari, Italy
luigi.quaranta@uniba.it

Giulio Mallardi
University of Bari, Bari, Italy
giulio.mallardi@uniba.it

Fabio Calefato
University of Bari, Bari, Italy
fabio.calefato@uniba.it

Important Dates

Submission portal opens: January 7th, 2026 
Deadline for paper submission: March 31st, 2026
Latest acceptance deadline for all papers: June 15th, 2026