Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientistsu0027 expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stackholders how it was created. The main limitation of provenance tracking solutions is that they cannot cope with provenance capture and integration of domain and ML data processed in the multiple workflows in the lifecycle, while keeping the provenance capture overhead low. To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROVML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary. We show a practical use in a real case in the Ou0026G industry, along with its evaluation using 239,616 CUDA cores in parallel.

Dont Forget Your Roots! Using Provenance Data for Transparent and Explainable Development of Machine Learning Models Explaining reasoning and behaviour of artificial intelligent systems to human users becomes increasingly urgent, especially in the field of machine learning. Many recent contributions approach this issue with posthoc methods, meaning they consider the final system and its outcomes, while the roots of included artefacts are widely neglected. However, we argue in this position paper that there needs to be a stronger focus on the development process. Without insights into specific design decisions and meta information that accrue during the development an accurate explanation of the resulting model is hardly possible. To remedy this situation we propose to increase process transparency by applying provenance methods, which serves also as a basis for increased explainability.