Juneberry automates the training, evaluation, and comparison of multiple ML models against multiple datasets. This makes the process of verifying and validating ML models more consistent and rigorous, which reduces errors, improves reproducibility, and facilitates integration.
Machine learning (ML) is increasingly being applied to cybersecurity, logistics, threat detection and analysis, and other critical, data-intensive operations. Evaluating machine learning models to improve results is challenging but necessary in these fields where precisely predicting outcomes is essential.
The Juneberry system facilitates machine learning experimentation by helping users train and compare machine learning models that may have different architectures, datasets, and/or hyperparameters. Various configuration files, in JSON format, control the characteristics of the experiment, such as which models to use, training datasets, evaluation datasets, and the types of reports and graphics to generate for comparison purposes. By automating training and evaluation, Juneberry can improve robustness and security, qualities foundational to AI engineering.