Ray Datasets: Scalable data preprocessing for distributed ML

Register for the webinar and get a first-hand look at how Ray Datasets:

• Provides hyper-scalable parallel I/O to most popular storage backends and file formats

• Supports common last-mile preprocessing operations, including basic parallel data transformations such as map, batched map, and filter, and global operations such as sort, shuffle, groupby, and stats aggregations

• Efficiently integrates with data processing libraries (e.g., Spark, Pandas, NumPy, Dask, Mars) and machine learning frameworks (e.g., TensorFlow, Torch, Horovod)

LinkResources

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• Q&A >>

• Ray GitHub >>

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