Bootstrapping Data Analytics Pipelines in Industrial Projects
13 May 2022
Prof. Matteo Brunelli
Dipartimento di Ingegneria Industriale
Jyrki Savolainen, School of Business and Management, LUT University, Finland
Data-based algorithms and machine learning (ML) algorithms, such as deep learning and neural networks, are finding their way into industrial applications. However, the possibilities for organizations to effectively implement these, and other state-of-the-art, methods on top of raw data in their ML-pipelines are seldom addressed. This is because the central interest of the researchers is most often on the theoretical development of methods using ready-made, clean and curated datasets. In this seminar, an illustrative example of an industrial-scale data-analytics development project is presented and discussed to illustrate some of the practical challenges of data-analytics implementation.