Short professional bio: Eva Stejskalová is the co-founder and managing director of MicroStep, a leading producer of highly automated CNC plasma and laser cutting machines based inSlovakia. The company delivers advanced solutions to major manufacturing and engineering firms in more than 60 countries worldwide.

Eva studied at the Slovak Technical University in Bratislava, where she later began her professional career as an assistant lecturer at the Department of Automation and Control. Following the political changes in 1989, she and a group of colleagues left academia to establish MicroStep. Over the years, she has held various key responsibilities within the company, including R&D and finance. In 2012, she earned her MBA degree from the Open University in Milton Keynes, England.

Presentation topic: Fast and Efficient Data Processing in AI-Driven CNC Manufacturing

Presenatation summary: This presentation explains how MicroStep’s CNC cutting machines and its proprietary CyberFab control system generate large volumes of operational and process data, and how this data can be processed efficiently using real-time processing, edge computing, artificial intelligence, and secure cloud infrastructure. It demonstrates how intelligent data processing enables predictive maintenance, process optimization, and improved machine performance, while forming the foundation for increasingly smart and autonomous production. Additionally, it highlights how leveraging machine data opens opportunities for new business models, such as data-driven services, predictive maintenance offerings, remote diagnostics, and performance-based service solutions, supporting innovation and long-term competitiveness at MicroStep.

Short professional bio: Peter Kissa is a CTO and software/product leader focused on building scalable digital solutions and AI-enabled data platforms. He leads architecture and delivery across multiple product lines and customer environments, with strong experience in cloud-native engineering, data integration, and applied AI. He also drives EU-funded R&D initiatives spanning trustworthy data pipelines, governance, and secure-by-design platform capabilities.

Presenatation summary: The presentation introduces SPICE from an end-user perspective: what the platform will enable, who it is for, and how it creates value in real operational settings. We will walk through the SPICE “destination” as a trustworthy and explainable data-pipeline platform spanning cloud and edge environments, and show how it reduces delivery complexity, improves time-to-insight, and supports compliant collaboration across organizations. Participants will leave with a clear understanding of the expected benefits, key privacy and trust considerations, practical preparation steps (data readiness, governance, roles), and an overview of adoption and monetization paths that support sustainability beyond the project.

Short professional bio:A Leading Scientist at the Institute of Informatics of the Slovak Academy of Sciences in Bratislava, Ladislav Hluchý has been active in research since 1975. They have led the Department of Parallel and Distributed Information Processing since 1986 and served as Director of the Institute of Computer Systems and its successor, the Institute of Informatics, from 1992 to 2015; they have been Deputy Director since 2015. Their work includes extensive leadership in international and national research collaboration, with roles as participant or team leader in 35 international projects across EU Framework Programmes (FP4–FP7), Horizon 2020, and Horizon Europe (including coordination of two FP6 projects), as well as major initiatives with EDA and NATO. They have also coordinated or contributed to more than 30 significant Slovak national projects (VEGA, APVV, ŠPVV, OPVaV).

Presentation topic:  Leveraging AI for Intelligent Data-Pipeline Composition:

Presenatation summary: This presentation provides an in-depth perspective on the AI capabilities embedded within the SPICE platform to enable intelligent, knowledge-driven data-pipeline composition. We introduce an advanced approach that combines Large Language Models (LLMs) and agentic AI to transform high-level textual descriptions of application requirements into fully executable data pipelines composed of reusable, containerized operators. The platform streamlines pipeline design, reduces engineering effort, and accelerates time-to-deployment.

The session further outlines the architectural foundations of AI-based pipeline operators that integrate model training and inference directly into application workflows. These operators are designed as reusable, parametrizable, and marketplace-ready components, ensuring portability and seamless deployment across heterogeneous cloud–edge environments.

Through a pilot use case demonstration, participants will gain practical insight into how AI-driven composition and modular AI components converge to deliver scalable, trustworthy, and business-ready data solutions, advancing pipeline engineering toward a more autonomous and adaptive paradigm.

Short professional bio: Zahra Najafabadi Samani is a Postdoctoral Researcher and Lecturer at the University of Innsbruck, Austria. She earned her PhD from the University of Klagenfurt, Austria. She has contributed to several national and European Union research projects, serving as a researcher and work package leader. She has also acted as a reviewer and Program Committee member for numerous international conferences and scientific journals. Her research focuses on resource management and performance optimization, with particular emphasis on AI-driven approaches within the Cloud-Edge continuum.

Presentation topic: Data Pipelines Orchestration Across the Computing Continuum:

Presentation summary: This talk addresses the challenges of managing distributed data pipelines across the heterogeneous IoT-edge-cloud continuum. As modern data-intensive applications increasingly span the computing continuum, effective orchestration, scalability, and resource management become critical. The discussion highlights the role of platform-independent workflows, continuous monitoring, predictive analytics, and adaptive scaling in enabling resilient, efficient, and service-level-aware data processing across complex distributed systems.