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Offline reinforcement learning

Introduction_to_Ray_Serve.pdf

Simplify and scale your XGBoost model using Ray on Anyscale

Getting started with Anyscale concepts, code, and clusters

Large Scale Distributed Training with TorchX and Ray

[Ray Meetup] Ray Train, PyTorch, TorchX, and distributed deep learning

Ray Datasets: Scalable data preprocessing for distributed ML

Ray Train: Production-ready distributed deep learning

The 2021 Year in Review of Ray

DeltaCAT: A Scalable Data Catalog for Ray Datasets

Unifying Preprocessing and Training at Scale with Ray Datasets

Faster time series forecasting using Ray and Anyscale

Introducing Anyscale: Accelerate scaling and deployment of AI / ML

RLlib: Scalable RL training and serving in the cloud with Ray

Scaling Machine Learning Workloads with Ray

Fast and efficient hyperparameter tuning with Ray Tune

Building a scalable ML model serving API with Ray Serve

[Webinar] An introduction to Ray for scaling machine learning (ML) workloads

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