I’m looking for help evaluating the efficacy of
mllint! If you have used
mllint, please consider filling in my 15-minute survey, as it is extremely important for my MSc thesis!
mllint is a command-line utility to evaluate the technical quality of Machine Learning (ML) and Artificial Intelligence (AI) projects written in Python by analysing the project’s source code, data and configuration of supporting tools.
mllint aims to …
- … help data scientists and ML engineers in creating and maintaining production-grade ML and AI projects, both on their own personal computers as well as on CI.
- … help ML practitioners inexperienced with Software Engineering (SE) techniques explore and make effective use of battle-hardended SE for ML tools in the Python ecosystem.
- … help ML project managers assess the quality of their ML and AI projects and receive recommendations on what aspects of their projects they should focus on improving.
mllint does this by measuring the project’s adherence to ML best practices, as collected and deduced from SE4ML and Google’s Rules for ML. Note that these best practices are rather high-level, while
mllint aims to give practical, down-to-earth advice to its users.
mllint may therefore be somewhat opinionated, as it tries to advocate specific tools to best fit these best practices. However,
mllint aims to only recommend open-source tooling and publically verifiable practices. Feedback is of course always welcome!
mllint is created during my MSc thesis in Computer Science at the Software Engineering Research Group (SERG) at TU Delft and ING’s AI for FinTech Research Lab on the topic of Code Quality and Software Engineering for Machine Learning projects
See also the
mllint-example-projectsrepository to explore the reports of an example project using
mllintto measure and improve its project quality over several iterations.
example-report.mdto view the report generated for the example project in the demo below.