The Best Radicalbit Alternatives in 2026

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

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

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

Cohere develops AI models and solutions for enterprise process automation, employee tools, and turning fragmented data into insights.

igel lets users train, test, and use machine learning models without writing code.

NannyML provides Python tools for post-deployment data science and monitoring model performance under data drift.

Baseten provides an inference platform for serving and scaling open-source and custom AI models in production.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

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

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.

Aim tracks machine learning experiments and describes itself as an open-source experiment tracker.

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.

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

Use Aeromancy to build reproducible machine learning workflows and projects.

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

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