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Juan Navas

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

hyperparameter-tuning-bayesian-optimization

What is hyperparameter tuning?

Chandler Gibbons

ray-1-10-thumb

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

Jules S. Damji

Fig 2 ml models blog

Considerations for deploying machine learning models in production: Part 2

Sriram Sankar

sriram-sankar-thumb

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

Matthew Deng

Amog Kamsetty

Richard Liaw

Richard Liaw headshot

Will Drevo

Distributed deep learning with Ray Train is now in Beta

Chaokun Yang

Yiming Yu

Figure2

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.  

forecasting blog 2 of 2 image 1

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

Miha Jenko

Heureka_Ray

Cost Effective Machine Learning with Ray

Michael Galarnyk

Local Machine Cloud

How to Speed Up XGBoost Model Training

Robert Nishihara

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Introducing Anyscale: The Future Is Distributed

ray 1.9

Ray version 1.9 has been released

Eric Liang

Eric Liang headshot

Chen Shen

Clark Zinzow

Waleed Kadous

3rdGenTasks andActors

Deep Dive: Data Ingest in a Third Generation ML Architecture

Jaikumar Ganesh

Why I joined Anyscale

New York City Yellow Taxi ride volumes per location 

Scaling Time Series Forecasting on Ray: ARIMA and Prophet on Ray

Anthony Naddeo

Danny Leybzon

figure 1

Running and Monitoring Distributed ML with Ray and whylogs

MLPatternsProduction

Considerations for Deploying Machine Learning Models in Production

Guyang Song

Yu Qi

C++ + Ray

Modern Distributed C++ with Ray

Ben Lorica

Ion Stoica

Ion Stoica headshot

Ray Ecosystem

What’s new in the Ray Distributed Library Ecosystem

BlogBiolexis on Anyscale (4)

Biolexis Boosts Their New AI/ML Drug Discovery Platform Using Anyscale’s Fully-Managed Ray Platform

Ray For Success

Four Reasons Why Leading Companies Are Betting On Ray

Anyscale logo (webinar thumbnail)

Ray Summit, the Industry Event for Scalable AI, Unveils New Innovations in Ray and the Anyscale Platform; Features Speakers from Uber, IBM, OpenAI, Shopify, Dow and Numerous Global Organizations

Blog Adtriba on Anyscale (4)

Adtriba Accelerates and Advances Media Mix Modeling Using the Anyscale Fully-Managed Ray Platform

This section is used to order the "Types" and "Tags" that show up for filters on the Blog Index

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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