Ray
MLOps & Infrastructure
Overview
Ray is an open-source distributed computing framework that provides a unified API for scaling Python applications and machine learning workloads. It supports distributed training, hyperparameter tuning, model serving, and reinforcement learning. Ray integrates with PyTorch, TensorFlow, and scikit-learn, and provides ML libraries including Ray Train, Ray Tune, and Ray Serve for production ML workflows.
- Pricing model
- Free
- Deployment
- Self-hosted / on-premises, SaaS (vendor-hosted)
- Regions served
- Global
- Categories
- Free, MLOps & Infrastructure, Software & technology, Workflow orchestration, Self-hosted / on-premises, Predictive / classical ML, Functional application, SaaS (vendor-hosted)
- Last updated
- 2 October 2026
- Published
- 2 October 2026
Use cases
Deployment options
Data sources & provenance
Where this information came from, how it was checked and when it was last looked at. “Last updated” records a change to the record — it does not mean the record was verified.
- Data quality
- Data quality: Editorially Reviewed
- Primary source
- ray.io
- Sources cited
- 2
- Last reviewed
- 28 September 2026
- Last updated
- 2 October 2026
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