Component-Based: Build encapsulated steps, then compose them to build complex pipelines.
Evolving State: Each pipeline step can fit, and evolve through the learning process
Hyperparameter Tuning: Optimize your pipelines using AutoML, where each pipeline step has their own hyperparameter space.
Compatible: Use your favorite machine learning libraries inside and outside Neuraxle pipelines.
Production Ready: Pipeline steps can manage how they are saved by themselves, and the lifecycle of the objects allow for train, and test modes.
Streaming Pipeline: Transform data in many pipeline steps at the same time in parallel using multiprocessing Queues.