Automate machine learning workflows with ε's powerful AutoML solution.
Optimize model performance with Bayesian search and random search strategies.
Automatically engineer and select the most relevant features for your dataset.
Leverage automated model selection across 50+ scikit-learn models and deep learning frameworks.
Install the AutoML Toolkit via pip.
pip install epsilon-automl
Use the simple API to search for the best-performing model.
from epsilon.automl import AutoML
automl = AutoML()
automl.fit(X_train, y_train)
best_model = automl.best_model
Export and deploy the optimized model for production.
automl.save("best_model.pkl")
Configure the search strategy, constraints, and evaluation metrics.
automl.search_space = {"max_iterations": 50}
automl.search_strategy = "bayesian"
| Task | AutoML | Manual Tuning | Accuracy Gain | Time Spent |
|---|---|---|---|---|
| Image Classification | 94.3% | 89.6% | +4.7% | 0.42h |
| Text Regression | 88.9% | 84.5% | +4.4% | 0.65h |
| Tabular Classification | 95.8% | 92.2% | +3.6% | 0.38h |
Save hundreds of hours on tuning and training with ε's industry-leading AutoML solution.