NEURAXLE

BUILD NEAT ML PIPELINES

Compatible with deep learning frameworks and the scikit-learn API, it can stream minibatches, use data checkpoints, build funky pipelines, and serialize models with custom per-step savers.

⚡️ 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.
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