CAIN 2024
Sun 14 - Mon 15 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Machine Learning (ML) - enabled systems capture new frontiers of industrial use. The development of such systems is becoming a priority course for many vendors due to the unique capabilities of Artificial Intelligence (AI) techniques. The current trend today is to integrate ML-functionality into complex systems as architectural components. There are a lot of relevant challenges associated with this strategy in terms of the overall system architecture and in the context of development workflow (MLOps). The probabilistic nature, crucial dependency on data and work in an environment of high uncertainty do not allow software engineers to apply traditional software development methodologies. As a result, there is a community request to systematize the most relevant experience in building software architectures with ML-components, to create new approaches to organizing the process of developing ML-enabled systems, and to build new models for assessing the system quality. Our research contributes to all mentioned directions and aims to create a methodology for the efficient implementation of ML-enabled software and AI components. The results of the study can be used in the design and development in industrial settings, as well as a basis for further studies in the research field, which is of both practical and scientific value.

Mon 15 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Doctoral Symposium and Energy-Aware AI EngineeringDoctoral Symposium / Research and Experience Papers at Pequeno Auditório
Chair(s): Justus Bogner Vrije Universiteit Amsterdam, Silverio Martínez-Fernández UPC-BarcelonaTech
11:00
6m
Talk
Software Design Decisions for Greener Machine Learning-based Systems
Doctoral Symposium
Santiago del Rey Universitat Politècnica de Catalunya (UPC)
11:06
6m
Talk
Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach
Doctoral Symposium
11:12
6m
Talk
Optimizing Data Analytics Workflows through User-driven Experimentation
Doctoral Symposium
Keerthiga Rajenthiram Vrije Universiteit Amsterdam
11:18
6m
Talk
Component-based Approach to Software Engineering of Machine Learning-enabled Systems
Doctoral Symposium
Vladislav Indykov Chalmers | University of Gothenburg
11:24
6m
Talk
Threat Modeling of ML-intensive Systems: Research Proposal
Doctoral Symposium
Felix Viktor Jedrzejewski Blekinge Institute of Technology
11:30
6m
Talk
Continuous Quality Assurance ML Pipelines under the AI Act
Doctoral Symposium
Matthias Wagner Lund University
11:36
10m
Talk
Green Runner: A tool for efficient deep learning component selection
Research and Experience Papers
Jai Kannan Applied Artificial Intelligence Institute, Deakin University, Scott Barnett Applied Artificial Intelligence Institute, Deakin University, Anj Simmons , Taylan Selvi Applied Artificial Intelligence Institute, Deakin University, Luís Cruz Delft University of Technology
11:46
15m
Talk
Engineering Carbon Emission-aware Machine Learning Pipelines
Research and Experience Papers
Erik Johannes Husom SINTEF Digital, Sagar Sen , Arda Goknil SINTEF Digital
12:01
10m
Talk
Identifying architectural design decisions for achieving green ML serving
Research and Experience Papers
Francisco Durán Universitat Politècnica De Catalunya - Barcelona Tech, Silverio Martínez-Fernández UPC-BarcelonaTech, Matias Martinez Universitat Politècnica de Catalunya (UPC), Patricia Lago Vrije Universiteit Amsterdam
Pre-print
12:11
10m
Talk
Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Research and Experience Papers
Negar Alizadeh Universiteit Utrecht, Fernando Castor University of Twente and Federal University of Pernambuco
12:21
9m
Live Q&A
Energy: Q&A Session
Research and Experience Papers