The Best Aeromancy Alternatives in 2026

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

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

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

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.

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

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

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

Featureform turns existing data infrastructure into a virtual feature store.

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

Deepchecks tests and validates machine learning models and data, covering workflows from research through production.

CML provides continuous machine learning and CI/CD workflows for machine learning projects.

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

Ray provides an AI compute engine with a distributed runtime and libraries for machine learning workloads.

Evidently evaluates, tests, and monitors machine learning systems and data pipelines.

Feathr provides enterprises with a platform for data engineering and AI workflows.

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

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.