  Machine Learning Workflows (MLWfs) have become essential and a disruptive
approach in problem-solving over several industries. However, the development
process of MLWfs may be complicated, hard to achieve, time-consuming, and
error-prone. To handle this problem, in this paper, we introduce machine
learning workflow management (MLWfM) as a technique to aid the development and
reuse of MLWfs and their components through three aspects: representation,
execution, and creation. More precisely, we discuss our approach to structure
the MLWfs' components and their metadata to aid retrieval and reuse of
components in new MLWfs. Also, we consider the execution of these components
within a tool. The hybrid knowledge representation, called Hyperknowledge,
frames our methodology, supporting the three MLWfM's aspects. To validate our
approach, we show a practical use case in the Oil & Gas industry.
