Ray Use Cases#

This page indexes common Ray use cases for scaling ML. It contains highlighted references to blogs, examples, and tutorials also located elsewhere in the Ray documentation.

Batch Inference#

Batch inference refers to generating model predictions over a set of input observations. The model could be a regression model, neural network, or simply a Python function. Ray can scale batch inference from single GPU machines to large clusters.

Many Model Training#

Many model training is common in ML use cases such as time series forecasting, which require fitting of models on multiple data batches corresponding to locations, products, etc. Here, the focus is on training many models on subsets of a dataset. This is in contrast to training a single model on the entire dataset.

Model Serving#

Ray’s official serving solution is Ray Serve. Ray Serve is particularly well suited for model composition, enabling you to build a complex inference service consisting of multiple ML models and business logic all in Python code.

Hyperparameter Tuning#

Ray’s Tune library enables any parallel Ray workload to be run under a hyperparameter tuning algorithm. Learn more about the Tune library with the following talks and user guides.

Distributed Training#

Ray’s Train library integrates many distributed training frameworks under a simple Trainer API, providing distributed orchestration and management capabilities out of the box. Learn more about the Train library with the following talks and user guides.

Reinforcement Learning#

RLlib is an open-source library for reinforcement learning (RL), offering support for production-level, highly distributed RL workloads while maintaining unified and simple APIs for a large variety of industry applications. RLlib is used by industry leaders in many different verticals, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and many others.

ML Platform#

The following highlights feature companies leveraging Ray’s unified API to build simpler, more flexible ML platforms.

End-to-End ML Workflows#

The following are highlighted examples utilizing Ray AIR to implement end-to-end ML workflows.