MOSAIC, acomparison framework for machine learning models

TL;DR

MOSAIC is a Python framework that simplifies, accelerates, and enhances the process of developing, testing, and analyzing machine learning models through an integrated pipeline and comprehensive management tools.

Contribution

It introduces a comprehensive, user-friendly framework that streamlines machine learning workflows from model declaration to result visualization and management.

Findings

Facilitates faster development and testing of models.

Provides automated generation of performance metrics and figures.

Stores results efficiently for analysis and comparison.

Abstract

We introduce MOSAIC, a Python program for machine learning models. Our framework is developed with in mind accelerating machine learning studies through making implementing and testing arbitrary network architectures and data sets simpler, faster and less error-prone. MOSAIC features a full execution pipeline, from declaring the models, data and related hyperparameters within a simple configuration file, to the generation of ready-to-interpret figures and performance metrics. It also includes an advanced run management, stores the results within a database, and incorporates several run monitoring options. Through all these functionalities, the framework should provide a useful tool for researchers, engineers, and general practitioners of machine learning.