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Misha Laskin

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The reinforcement learning framework

Simon Mo

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Chandler Gibbons

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Serving PyTorch models with FastAPI and Ray Serve

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An informal introduction to reinforcement learning

Antoni Baum

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Three ways to speed up XGBoost model training

Juan Navas

Richard Liaw

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Learn how to use the Ray Tune library to distribute hyperparameter tuning tasks among several computers.

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How to Distribute Hyperparameter Tuning Using Ray Tune

How to distribute hyperparameter tuning using Ray Tune

Clark Zinzow

Alex Wu

Jiajun Yao

Eric Liang

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Chen Shen

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Ray Datasets for large-scale machine learning ingest and scoring

Explore a practical hands-on case showing how to tune hyperparameters on XGBoost. Learn how to easily deploy and optimize ML models.

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Guide to XGBoost Hyperparameter Tuning

How to tune hyperparameters on XGBoost

In part 1 of a blog series, we cover the basics of understanding what hyperparameters are and how they're utilized in controlling ML model behavior.

The Bayesian optimization method treats the search for the optimal hyperparameters as an optimization problem. 

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What is Hyperparameter Tuning? | Anyscale

What is hyperparameter tuning?

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Ray 1.10: Windows support beta, enhanced job submission, and more

Jules S. Damji

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Considerations for deploying machine learning models in production: Part 2

Sriram Sankar

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Why I joined Anyscale: The vision, the tech, and the team

Matthew Deng

Amog Kamsetty

Will Drevo

Distributed Deep Learning with Ray Train is Now In Beta

Distributed deep learning with Ray Train is now in Beta

Chaokun Yang

Yiming Yu

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Building an end-to-end ML pipeline using Mars and XGBoost on Ray

Christy Bergman

Displaying [New York City Yellow Taxi](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) ride volumes, with 1 week hourly forecast.  Blue=observed, Orange=predicted, per validation dataset.  Forecast generated using Google’s Temporal Fusion Transformer algorithm implemented by Pytorch forecasting, and parallelized by Ray for faster runtime, either on a laptop or on any cloud.  Image by Author.  

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Time Series Forecasting using an LSTM version of RNN with PyTorch Forecasting and Torch Lightning

Miha Jenko

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Cost Effective Machine Learning with Ray

Anyscale + Astronomer

Scalable Machine Learning with Anyscale and Astronomer

Unlocking the Power of Scalable Machine Learning with Anyscale and Astronomer

At Ray Summit 2024, we brought together AI experts and practitioners to dive into the future of distributed AI and scalable machine learning.

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Ray Summit 2024: Breaking Through the AI Complexity Wall

Explore the new and improved Anyscale Workspaces. Get advanced observability features so you spend less time on busywork and more time building AI apps.

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Explore Anyscale’s New UX

Anyscale’s New User Experience: A Comprehensive Overview

With Anyscale on AWS Marketplace, you can build, scale, and optimize AI/ML and Generative AI—all within the trusted AWS cloud environment.

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Anyscale Available on AWS Marketplace

Anyscale Now Available on AWS Marketplace and Achieves Generative AI Competency

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