Rubix ML 3.0 and 3.1 Released With New Machine Learning Capabilities
The latest updates to the Rubix ML library for PHP introduce parallel execution, neural network enhancements, and new clustering algorithms to support integrated predictive analysis.
Rubix ML 3.0 and 3.1 are out. These versions bring new machine learning capabilities to PHP developers, focusing on performance, algorithm support, and data processing inside the application layer.
Version 3.0 adds parallel execution for selected algorithms to handle compute-heavy tasks. It also ships with new features for training neural networks, plus enhancements for dataset management and model performance evaluation.
Version 3.1 builds on 3.0 by adding K-medoids clustering. It introduces new data extractors, learning rate schedulers, and built-in image transformations.
These updates let developers experiment with machine learning models and integrate predictive analysis directly into their codebase alongside business logic, eliminating the need for separate infrastructure or external services. You can find detailed documentation in the official repository.