Find Open-Source Alternatives
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Compare community-driven replacements for H2O Driverless AI in automl tools workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
These projects match the most common migration paths for teams replacing H2O Driverless AI.
Why teams pick it
Seamless cloud deployment via SageMaker and Docker
Recent commits in the last 6 months
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to H2O Driverless AI.
Why teams pick it
End‑to‑end pipeline automation reduces manual effort
Automated Machine Learning library for fast, robust model pipelines
Why teams choose it
Watch for
Requires a Python environment; no graphical UI
Migration highlight
Customer churn prediction
Automatically preprocess telecom data, select predictive features, tune a LightGBM model, and generate interpretable churn risk scores.
AutoML for deep learning that requires no coding expertise
Automated ML delivering top performance in just three lines
Automated, transparent machine learning for tabular data in minutes
Automated ML pipelines powered by genetic programming.
Hands-free AutoML that plugs directly into scikit-learn
Fast, lightweight AutoML and hyperparameter tuning for Python
Teams replacing H2O Driverless AI in automl tools workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from H2O Driverless AI.
Why teams choose it
Watch for
Requires Python ≥ 3.7 and TensorFlow ≥ 2.8
Migration highlight
Image classification for a startup's product catalog
Trains a high‑accuracy classifier in minutes without writing custom CNN code.
Why teams choose it
Watch for
Higher memory and compute usage compared to hand‑crafted models
Migration highlight
Customer churn prediction (tabular)
Accurately predicts churn risk, enabling targeted retention campaigns with minimal coding.
Why teams choose it
Watch for
Focused on tabular data; not suitable for image or audio tasks
Migration highlight
Quick baseline generation
Produces a set of candidate models with performance metrics and a ready‑to‑use report within minutes.
Why teams choose it
Watch for
Requires Python 3.10+ and several heavy dependencies.
Migration highlight
Rapid baseline generation for new datasets
TPOT discovers a performant scikit‑learn pipeline in minutes, providing a strong starting point for further refinement.
Why teams choose it
Watch for
Higher computational cost than manual tuning
Migration highlight
Rapid prototyping of classification models
Generate a performant classifier in minutes without manual hyperparameter tuning
Why teams choose it
Watch for
Primary implementation is Python‑only; .NET version is separate
Migration highlight
Rapid tabular classification baseline
Generate a high‑accuracy classifier in minutes, reducing model development time