Knowledge Management for Data Science
KMDS, Knowledge Management for Data Science, is a Python library to document your analytics and machine learning workflows. The knowledge captured as part of your development process is stored as a RDF graph. These graphs can be shared between projects, or, updated sequentially by different iterations of the same project. These graphs can be searched and manipulated. At present, the library provides a set of convenient methods to retrieve observations recorded in each phase of a data science project. This basic set of reporting features is a start. Users can find other novel ways to use this graph and the usefulness of the graph should evolve. The documentation for KMDS is organized based on concepts and recipes. The concepts section describes the ideas that form the building blocks for capturing knowledge for your use case. The recipes section illustrates a set of concepts in a particular real-world use case. The recipes provided collectively cover all the concepts discussed in the concepts section.