Supercharge your ZenML pipelines with LightGBM's fast and efficient gradient boosting
Integrate LightGBM, a high-performance gradient boosting framework, seamlessly into your ZenML pipelines for optimized machine learning workflows. Leverage LightGBM's speed, efficiency, and ability to handle large-scale datasets to boost your model training and prediction tasks within the structured environment of ZenML.
from zenml import pipeline, step
from zenml.integrations.lightgbm.steps import lightgbm_trainer_step
@step
def load_data():
# Load and preprocess the dataset
train_data = ...
test_data = ...
return train_data, test_data
@pipeline
def lightgbm_pipeline():
train_data, test_data = load_data()
lightgbm_trainer_step(
train_data=train_data,
test_data=test_data,
params={
'objective': 'binary',
'metric': 'auc',
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
)
if __name__ == "__main__":
# Run the pipeline
lightgbm_pipeline()
Expand your ML pipelines with more than 50 ZenML Integrations
