Open-source Dataiku alternatives

MLflow is an open-source AI engineering platform for agents, LLM applications and ML models. It helps teams debug, evaluate, monitor and optimize AI applications in production while keeping control of costs and access to models and data. It is written in Python, released under the Apache-2.0 license, and has been developed since 2018, with topics covering MLOps, model management and LLMOps.

For LLM and agent work it offers production observability with traces, plus evaluation, prompt management and prompt optimization, and an AI Gateway that governs costs and model access. It works with Python, TypeScript and JavaScript, Java and other languages, and integrates with OpenTelemetry and MCP. Getting started means starting an MLflow server, enabling logging in your code and running it, then exploring traces and metrics in the web UI on port 5000. A setup wizard can connect to an MLflow server or a Databricks workspace and add tracing with a coding agent.

Orange is a data mining and visualization toolbox aimed at both beginners and experienced analysts. Instead of writing code, users assemble analyses as workflows on a canvas, which hides the underlying mechanics and exposes intuitive concepts. The project's stated goal is that anyone who owns data should be able to explore it.

The toolbox covers interactive data visualization along with classic machine learning methods such as classification, regression, clustering, decision trees and random forests. It is written in Python on top of scikit-learn, NumPy, SciPy and pandas, and extra functionality is added through add-ons installed from the menu bar; developers can write their own widgets from an example template.

Windows and macOS users can download a standalone installer, while Linux users and developers can install with conda, pip or uv, and winget is available on Windows. The project is open source, welcomes new widgets and contributions, and has a community on Discord.

TPOT, which stands for Tree-based Pipeline Optimization Tool and is published by Epistasis Lab, is a Python automated machine learning library. It uses genetic programming to explore and optimize machine learning pipelines, automating steps such as model selection, feature engineering and hyperparameter tuning, and its authors describe it as a data science assistant.

The current version is a ground-up rewrite, previously known as TPOT2, aimed at better efficiency and performance. It adds genetic feature selection, a more flexible way to define search spaces, multi-objective optimization and a modular framework for customizing the evolutionary algorithm. The README cites an academic paper describing the graph-based implementation.

TPOT is released under the LGPL-3.0 license and installs as a Python package, with documentation on the project website. The present version was developed by a team at Cedars-Sinai. It is a library you run in your own Python environment rather than a hosted service.