Component-based Approach to Software Engineering of Machine Learning-enabled Systems
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 AprDisplayed 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 6mTalk | Software Design Decisions for Greener Machine Learning-based Systems Doctoral Symposium Santiago del Rey Universitat Politècnica de Catalunya (UPC) | ||
11:06 6mTalk | Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach Doctoral Symposium | ||
11:12 6mTalk | Optimizing Data Analytics Workflows through User-driven Experimentation Doctoral Symposium Keerthiga Rajenthiram Vrije Universiteit Amsterdam | ||
11:18 6mTalk | Component-based Approach to Software Engineering of Machine Learning-enabled Systems Doctoral Symposium Vladislav Indykov Chalmers | University of Gothenburg | ||
11:24 6mTalk | Threat Modeling of ML-intensive Systems: Research Proposal Doctoral Symposium Felix Viktor Jedrzejewski Blekinge Institute of Technology | ||
11:30 6mTalk | Continuous Quality Assurance ML Pipelines under the AI Act Doctoral Symposium Matthias Wagner Lund University | ||
11:36 10mTalk | 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 15mTalk | Engineering Carbon Emission-aware Machine Learning Pipelines Research and Experience Papers | ||
12:01 10mTalk | 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 10mTalk | 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 9mLive Q&A | Energy: Q&A Session Research and Experience Papers |