Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text.
Starting today, the SageMaker LightGBM algorithm offers distributed training using the Dask framework for both tabular classification and regression tasks. They’re available through the SageMaker Python SDK. The supported data format can be either CSV or Parquet. Extensive benchmarking experiments on three publicly available datasets with various settings are conducted to validate its performance.
Customers are increasingly interested in training models on large datasets with SageMaker LightGBM, which can take a day or even longer. In these cases, you might be able to speed up the process by distributing training over multiple

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