ray

Ray addresses the challenges of scaling machine learning workloads across distributed systems, making it easier for engineers to deploy and manage complex AI applications. It is particularly beneficial for ML/AI teams, including data scientists and machine learning engineers, who require a robust framework for hyperparameter optimisation and large language model deployment. With over 41,000 stars and a steady growth of 1,499 stars in 96 days, Ray demonstrates strong community interest and maturity, indicating it is a production-ready solution. However, teams focusing on simpler ML tasks or those with minimal infrastructure may find it more complex than necessary.

Ray is a strong choice when teams need to scale their machine learning models efficiently across multiple nodes, particularly for deep learning and hyperparameter tuning tasks. Teams should consider alternatives if their projects are smaller in scope or if they lack the infrastructure to support a distributed system.

Ray is primarily used by machine learning engineers, data scientists, and AI researchers who require a powerful open source tool for engineering teams. It is commonly integrated into products and systems that involve large-scale data processing, AI model training, and deployment in production environments.

Latest commit: 2026-02-14. Over the past 97 days, this repository gained 1.5k stars (+3.8% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.