The Best Butterfree Alternatives in 2026

PySyft supports data science on data that remains on another person’s server.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

Maze provides an applied reinforcement learning framework for developing projects in Python.

Use Aeromancy to build reproducible machine learning workflows and projects.

Pachyderm provides data-focused pipelines and version control for datasets and related workflows.

Featureform turns existing data infrastructure into a virtual feature store.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

PyToune provides a simplified framework and utilities for working with PyTorch.

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

FLAML provides a fast Python library for automated machine learning and tuning.

The Feast repository describes an open source feature store for AI and machine learning projects.

FlexML offers an easy to use and flexible automated machine learning library for Python.

ZenML provides a platform for machine learning pipelines and AI agents.

The goro project provides a high level machine learning library written for Go.

This Docker-based starter kit provides a runtime for machine learning work in Jupyter notebooks.

PrimeHub offers an open-source platform for managing machine learning operations and workflows.

Deploy a ML inference service on a budget in less than 10 lines of code.

The Feast project provides an open source feature store for AI and machine learning.

DyNet provides tools for building dynamic neural networks and deep learning models.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

Ludwig provides a low code framework for building custom LLMs, neural networks, and other AI models.