ray-data

To process datasets larger than memory for ML pipelines, you can use streaming execution to ingest and transform data across distributed CPU and GPU resources. This enables efficient data preparation without loading the entire dataset into RAM.

Yes, you can use Ray Data to read and transform Parquet files for batch inference. It supports reading various formats including Parquet, CSV, and JSON, allowing you to apply distributed transformations across multiple nodes.

Ray Data integrates seamlessly with PyTorch and TensorFlow for model training. It facilitates efficient data ingestion and transformation, preparing the data directly for your training and batch inference workloads.

The best way to scale ETL transformations across multiple CPU nodes is using distributed data processing. This approach supports parallel processing, enabling you to scale data preparation and transformation across your cluster effortlessly.

To process terabytes of image data for a computer vision model, you can distribute image decoding and augmentation steps across a cluster of machines. This scalable data processing handles large datasets efficiently using streaming execution.

Yes, you need pandas and pyarrow installed as dependencies to run distributed data processing with Ray Data. These libraries provide the underlying data structures and format support required for transformations.