Abstract:

Achieving key objectives, like faster serving time, less effort, cheaper operation cost, and higher reliability, is of paramount importance in developing and operating machine learning (ML) systems. Such key objectives can be formulated in the aspect of Quality of Analytics (QoA) in which we can view “analytics”, in a broad sense, as a “workflow“ to produce “results” for decision making, such as whether to use a trained ML model for production or an inference/prediction to improve quality of experience in a mobile network. Thus, we can examine QoA for ML from the view of (i) the engineering process, (ii) the artifact as the output of the process, and (iii) the service encapsulating the process/artifact. In this talk, first we will formulate the view of quality of analytics in ML based on ML processes, artifacts and services. Second, we will discuss the challenges and approaches of supporting QoA. To tackle these challenges, we will present two of our current works on QoA for ML service deployment in the edge system and for the quality and cost awareness in federated machine learning marketplaces.

Biography:

Hong-Linh Truong is a tenured associate professor at the Department of Computer Science at School of Science, Aalto University, Finland. He leads the AaltoSEA Group on Systems and Services Engineering Analytics. He is also a Priv.-Doz. (Adjunct Associate Professor) at the Faculty of Informatics, TU Wien. He received a PhD (2005) and a Habilitation (2013) from TU Wien, Austria. Among his visiting positions, he was a visiting scholar at University of California, Irvine and at University of Southern California and had short visits to National Institute of Informatics (NII), Japan and lectured in Fudan University and HoChiMinh City University of Technology as a guest professor.

His main research interest focuses Systems, Software, Data and Service Engineering Analytics by developing novel techniques and tools for monitoring, analyzing, and optimizing functions, performance, data quality, elasticity, and uncertainties associated with systems, software, data and services. His research has been applied to: Monitoring, Analysis and Optimization Techniques for Programs, Data and Systems; Parallel, Grid and Cloud Computing, and IoT; Big Data; Data Service Models and Analytics; Socio-technical Services Engineering; and Elastic Computing. Furthermore, he is interested in (free) ICT solutions for (under) developing countries. He had delivered several invited talks and he published more than 200 refereed papers in books, conferences/workshops and journals (Google Scholar citations=6544, h-index=41). He (co)receives an outstanding paper award, seven best paper awards, one best paper run-up award, and one best poster award. Contact him at linh.truong@aalto.fi (https://users.aalto.fi/~truongh4/).