Individual skill
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
05-data-processing/ray-data/SKILL.md
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- ai Repository topic
- ai-research Repository topic
- claude Repository topic
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- codex Repository topic
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- data-processing Declared skill tag
- ray-data Declared skill tag
- distributed-computing Declared skill tag
- ml-pipelines Declared skill tag
- batch-inference Declared skill tag
- etl Declared skill tag
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Read excerpt
# Ray Data - Scalable ML Data Processing
Distributed data processing library for ML and AI workloads.
## When to use Ray Data
**Use Ray Data when:**
- Processing large datasets (>100GB) for ML training
- Need distributed data preprocessing across cluster
- Building batch inference pipelines
- Loading multi-modal data (images, audio, video)
- Scaling data processing from laptop to cluster
**Key features**: