Next-Gen Middleware for MLOps in Distributed Systems

About the Workshop

Welcome to the 2^nd International workshop on Next-Gen Middleware for MLOps in Distributed Systems (MIND). MIND will be hosted in conjunction with the 27th ACM International Middleware Conference conference, which will be held in Universitat Rovira i Virgili Tarragona, Spain from 14th – 18th December 2026.

This workshop aims to bring together researchers, practitioners, and industry stakeholders to explore middleware innovations that support MLOps in distributed systems. It will focus on practical solutions to real-world challenges in orchestrating end to end ML pipelines, from data collection to model deployment and continuous monitoring in dynamic, heterogeneous, and resource constrained environments.

History

The Next-Gen Middleware for MLOps in Distributed Systems (MIND) workshop was successfully held on December 15, 2025. It brought together researchers and practitioners to discuss scalable, reliable, and efficient middleware solutions for MLOps in distributed systems, with a strong focus on trustworthy AI, distributed training, and HPC-enabled AI inference.

The program included a keynote on safeguarding AI systems and paper presentations covering topics such as scalable inference on HPC infrastructure, fault tolerance in distributed training, and diversity-aware graph algorithms.

Please check the previous version of the workshop at: MIND2025.

Call for Papers

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, DevOps, and data engineering to streamline the end to end lifecycle of ML systems. It covers the full spectrum of activities involved in operationalizing ML, such as data collection, preprocessing, model training, validation, deployment, monitoring, and continuous improvement, ensuring scalability, reliability, and efficiency in production environments.

However, challenges such as large-scale data transfer to cloud, limited bandwidth, latency, and privacy concerns have increased the need for effective MLOps in distributed systems. In these settings, ML workflows must be managed and automated across a combination of cloud, fog, and edge environments. Implementing MLOps in such hybrid infrastructures requires addressing the inherent complexity of distributed pipelines while maintaining system performance, model reliability, and data security.

To meet these demands, middleware has emerged as a critical enabler. Middleware provides a layer of abstraction and coordination that simplifies the deployment, monitoring, and management of ML models across distributed components. It handles resource discovery, workload scheduling, communication management, and fault tolerance, while also integrating with tools for version control, experiment tracking, and model retraining.

We invite submissions on a wide range of topics including, but not limited to:

Topics of Interest

Middleware-supported communication protocols for scalable and efficient MLOps across distributed systems
Middleware for dynamic resource management and orchestration in heterogeneous ML environments
Middleware-driven model compression and optimization for efficient deployment in resource-constrained settings
Middleware solutions for deploying, scaling, and monitoring ML workloads across hybrid infrastructures
Lightweight and energy-efficient middleware frameworks for sustainable MLOps
Trustworthy and privacy-preserving middleware systems for secure ML operations
APIs for middleware-based development of distributed MLOps applications
Middleware-supported migration and fault-tolerant mechanisms for robust ML service availability
Middleware support for federated learning and decentralized model training workflows

Important Dates

Paper Submission Deadline: September 10, 2026
Notification of Acceptance: October 12, 2026
Workshop Papers Camera-Ready: October 16, 2026

*All deadlines are Anywhere on Earth (AoE).

Submission Guidelines

Authors are invited to submit original and unpublished work, which must not be submitted concurrently for publication elsewhere, in the following format:

Full paper (6 pages): Interesting, novel results or completed work on topics within the scope of the workshop.
Short paper (4 pages): Exciting preliminary work or novel ideas in their early stages.
Poster (2 pages): Working, presentable systems or brief explanations of a research project.

The page length includes figures, tables, appendices, and references. The font size has to be set to 9pt. Papers exceeding this page limit or with smaller fonts will be desk-rejected without review. Submitted papers must adhere to the formatting instructions of the ACM SIGCONF style, which can be found on the ACM template page.

For each accepted paper, at least one author is required to register and attend the workshop in-person to present their poster/paper on-site. The Middleware 2026 conference proceedings will be published in the ACM Digital Library.

Submission Portal: Submit papers via HotCRP.