RAY - RAY: The AI Compute Engine
Ray manages, executes, and optimizes compute needs across AI workloads. It unifies infrastructure via a single, flexible framework—enabling any AI workload from data processing to model training to model serving and beyond.
RAY Product Information
What is RAY?
RAY is the leading AI compute engine seamlessly scaling from laptops to the cloud with its Python-native API and unmatched precision in coordinating heterogeneous resources. It offers a suite of ML libraries for developers and excels in executing AI workloads, enabling users to focus on core machine learning tasks.
How to Use RAY?
1. Sign up to avail expert help and accelerate your journey with RAY. 2. Leverage RAY's Python-native API and ecosystem integrations including ML frameworks, specialized libraries, and ML Ops tools. 3. Utilize RAY's suite of ML libraries for distributed and unstructured data processing, model training, hyperparameter tuning, and model deployment and serving. 4. Engage in end-to-end machine learning workflows with RAY's capabilities in data preprocessing, offline batch inference, distributed model training and reinforcement learning.
Core Features of RAY
- Seamless Scaling
- Python Native API
- Ecosystem Integrations
- Unmatched Precision in Resource Coordination
- Suite of ML Libraries
- Distributed AI Workload Execution
Use Cases of RAY
- Data Preprocessing
- Model Training
- Hyperparameter Tuning
- Model Deployment and Serving
- Reinforcement Learning
- End-to-End LLM Workflows
FAQ about RAY
What is RAY's primary advantage in executing AI workloads?
RAY offers seamless scalability and Python-native API along with unmatched precision in coordinating heterogeneous resources, enabling efficient execution of AI workloads.
What are the available ML libraries within RAY?
RAY provides ML libraries such as Ray Data, Ray Train, Ray Tune, Ray Serve, and Ray RLLIB, facilitating various aspects of distributed AI workloads.
What use cases does RAY support?
RAY supports various use cases, including data preprocessing, model training, hyperparameter tuning, model deployment and serving, reinforcement learning, and end-to-end LLM workflows.
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