Ray is a distributed computing framework for scaling machine learning and AI workloads to production, offered on Anyscale with improved developer tooling, stable autoscaling, cost optimizations, and expert support.

Key Features:

• Managed Ray on Anyscale for production-ready cluster orchestration and autoscaling
• Improved developer tooling and integrations for building and deploying distributed ML/AI apps
• Cost optimization and expert support to maintain stable, efficient runtime environments

Use Cases:

• Scaling model training and hyperparameter tuning across many nodes
• Deploying and serving large AI models and real-time inference pipelines
• Distributed data processing and reinforcement learning at production scale

Benefits:

• Reliable production scaling and stability for mission-critical ML workloads
• Lower operational and infrastructure costs through efficient autoscaling and optimizations
• Faster developer productivity with better tooling and access to expert support