Ray
Ray is an open-source unified compute framework for scaling AI and Python applications, developed by Anyscale. It provides a simple API for parallel and distributed computing, enabling developers to scale workloads from a laptop to a large cluster without code changes. Ray includes libraries for distributed training (Ray Train), hyperparameter tuning (Ray Tune), reinforcement learning (Ray RLlib), and serving (Ray Serve).
Developers should learn Ray when building scalable machine learning or data-intensive applications that require distributed computing, such as training large models, running hyperparameter sweeps, or deploying AI services. It is particularly useful for teams transitioning from single-node to distributed setups, as it abstracts away cluster management complexities and integrates with popular ML frameworks like TensorFlow and PyTorch.