MLJ: A Julia package for composable machine learning

Statistical modeling, and the building of complex modeling pipelines, is a cornerstone of modern data science. Most experienced data scientists rely on high-level open source modeling toolboxes - such as sckit-learn [1]; [2] (Python); Weka [3] (Java); mlr [4] and caret [5] (R) - for quick blueprinting, testing, and creation of deployment-ready models. They do this by providing a common interface to atomic components, from an ever-growing model zoo, and by providing the means to incorporate these into complex workflows. Practitioners are wanting to build increasingly sophisticated composite models, as exemplified in the strategies of top contestants in machine learning competitions such as Kaggle. MLJ (Machine Learning in Julia) [18] is a toolbox written in Julia that provides a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing machine model implementations written in Julia and other languages. More broadly, the MLJ project hopes to bring cohesion and focus to a number of emerging and existing, but previously disconnected, machine learning algorithms and tools of high quality, written in Julia. A welcome corollary of this activity will be increased cohesion and synergy within the talent-rich communities developing these tools. In addition to other novelties outlined below, MLJ aims to provide first-in-class model composition capabilities. Guiding goals of the MLJ project have been usability, interoperability, extensibility, code transparency, and reproducibility.