  Machine Learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science
and engineering domains, like geoscience, climate science, and health science.
In these domains, users need to perform comprehensive data analyses combining
scientific data and ML models to provide for critical requirements, such as
reproducibility, model explainability, and experiment data understanding.
However, scientific ML is multidisciplinary, heterogeneous, and affected by the
physical constraints of the domain, making such analyses even more challenging.
In this work, we leverage workflow provenance techniques to build a holistic
view to support the lifecycle of scientific ML. We contribute with (i)
characterization of the lifecycle and taxonomy for data analyses; (ii) design
principles to build this view, with a W3C PROV compliant data representation
and a reference system architecture; and (iii) lessons learned after an
evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946
GPUs. The experiments show that the principles enable queries that integrate
domain semantics with ML models while keeping low overhead (<1%), high
scalability, and an order of magnitude of query acceleration under certain
workloads against without our representation.
