ray-data

Process large datasets for distributed machine learning workloads with Ray.

6|3|Updated Jan 29, 2026

Search Tags:#data processing #distributed computing #ray #batch inference #ml pipelines

Ready to run

npx skills add https://github.com/jonnabio/ace-framework --skill ray-data-jonnabio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ray-data
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/ray-data
Command: npx skills add https://github.com/jonnabio/ace-framework --skill ray-data-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ray[data], pyarrow, pandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the challenge of processing and scaling large datasets, making it efficient for ML workloads and distributed computing tasks.

Core Features & Use Cases

Scalable Data Processing: Stream process large datasets, support multi-modal data types.
Framework Integration: Integrates with popular ML frameworks like Ray Train, PyTorch, TensorFlow.
Use Case: Ideal for scenarios like batch inference, data preprocessing, multi-modal data loading, and distributed ETL pipelines.

Quick Start

Install the ray-data skill and process large datasets with Ray.

Frequently Asked Questions about ray-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I scale data processing for large ML workloads?▼

Scalable data processing for ML workloads is handled by using distributed computing to stream large datasets, supporting multi-modal data types and enabling efficient batch inference and preprocessing across clusters.

Can I use Ray Data for distributed batch inference?▼

Yes, distributed batch inference is a supported use case. You can process large datasets across a cluster to execute batch inference, multi-modal data loading, and distributed ETL pipelines for machine learning workflows.

Does Ray Data integrate with PyTorch and TensorFlow for ML pipelines?▼

Yes, it integrates with popular ML frameworks like PyTorch and TensorFlow. This allows you to build distributed ML pipelines that stream large datasets directly into training and batch inference workloads.

How do I process multi-modal data types in a distributed computing environment?▼

You can process multi-modal data types by leveraging distributed computing features that stream large datasets. This approach supports multi-modal data loading and scalable preprocessing for complex machine learning pipelines.

Do I need pandas and PyArrow installed to run Ray Data?▼

Yes, pandas and PyArrow are required dependencies. They provide the underlying data structures and serialization support needed to handle large datasets and integrate them with various machine learning frameworks.