All Posts

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02 . 22 . 2022

An informal introduction to reinforcement learning

Reinforcement learning (RL) has played a critical role in the rapid pace of AI advances over the last decade. In this post, we'll cover what RL is and why it's important, both as a research subject and for a diverse set of practical applications.

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02 . 17 . 2022

Three ways to speed up XGBoost model training

In a previous blog post, we covered the advantages and disadvantages of several approaches for speeding up XGBoost model training. In this article, we’ll dive into three different approaches, with code snippets so you can follow along.

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02 . 15 . 2022

How to distribute hyperparameter tuning using Ray Tune

Want to tune hyperparameters more quickly without compromising quality? In this article, we’ll demonstrate how to use the Ray Tune library to distribute the hyperparameter tuning task among several computers.

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02 . 14 . 2022

Ray Datasets for large-scale machine learning ingest and scoring

We're happy to introduce Ray Datasets: A data loading and preprocessing library built on Ray that leverages Ray’s task, actor, and object APIs to enable large-scale machine learning ingest, training, and inference within a single Python application.

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02 . 09 . 2022

How to tune hyperparameters on XGBoost

In this hands-on article, we’ll explain how to tune hyperparameters on XGBoost. You just need to know some Python to follow along, and we’ll show you how to easily deploy machine learning models and then optimize their performance.

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02 . 08 . 2022

What is hyperparameter tuning?

Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. In this article, we’ll explore some examples of hyperparameters and delve into a few models for tuning hyperparameters.

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02 . 07 . 2022

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

Ray 1.10 is here! The highlights include Windows support — now in beta — and enhancements to Ray job submission, including log streaming and custom headers for authentication.

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02 . 04 . 2022

Considerations for deploying machine learning models in production: Part 2

In an earlier blog post, we shared five considerations for deploying machine learning models in production. In this post, we'll explore how to tune and train at scale and track model experiments.

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02 . 02 . 2022

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

When Sriram Sankar joined Anyscale, he found a technology and a company vision that he could get behind — but he also saw an exciting opportunity to use his past experiences to build something new.

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01 . 25 . 2022

Distributed deep learning with Ray Train is now in Beta

Introducing Ray Train, an easy-to-use library for distributed deep learning. In this post, we show how Ray Train improves developer velocity, is production-ready, and comes with batteries included.