  Machine Learning (ML) techniques are becoming essential components of many
software systems today, causing an increasing need to adapt traditional
software engineering practices and tools to the development of ML-based
software systems. This need is especially pronounced due to the challenges
associated with the large-scale development and deployment of ML systems. Among
the most commonly reported challenges during the development, production, and
operation of ML-based systems are experiment management, dependency management,
monitoring, and logging of ML assets. In recent years, we have seen several
efforts to address these challenges as witnessed by an increasing number of
tools for tracking and managing ML experiments and their assets. To facilitate
research and practice on engineering intelligent systems, it is essential to
understand the nature of the current tool support for managing ML assets. What
kind of support is provided? What asset types are tracked? What operations are
offered to users for managing those assets? We discuss and position ML asset
management as an important discipline that provides methods and tools for ML
assets as structures and the ML development activities as their operations. We
present a feature-based survey of 17 tools with ML asset management support
identified in a systematic search. We overview these tools' features for
managing the different types of assets used for engineering ML-based systems
and performing experiments. We found that most of the asset management support
depends on traditional version control systems, while only a few tools support
an asset granularity level that differentiates between important ML assets,
such as datasets and models.
