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Figure2
01 . 05 . 2022

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

The Ray team at Ant Group developed the Mars On Ray scientific computing framework. Combining Mars with XGBoost on Ray and other Ray machine learning libraries makes it easy to implement an end-to-end AI pipeline in one job, and use one Python script...

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12 . 21 . 2021

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

This blog, Part 2, will explain how to use Ray to speed up Deep Learning forecasting when training one large global model in order to predict many target time series. We will train an LSTM version of RNN with GRN building blocks, Encoder-Decoder, and...

Heureka_Ray
12 . 20 . 2021

Cost Effective Machine Learning with Ray

Why Heureka choose Ray for cost effective machine learning.

Local Machine Cloud
12 . 15 . 2021

How to Speed Up XGBoost Model Training

XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This post reviews some approaches for accelerating this process like changing tree construction me...

introducing-anyscale-thumbnail
12 . 07 . 2021

Introducing Anyscale: The Future Is Distributed

Announcing our $100M Series C and general availability of the Anyscale managed Ray offering.

ray 1.9
12 . 06 . 2021

Ray version 1.9 has been released

Ray version 1.9 has been released! Release highlights include: Ray Train is now in beta, Ray Datasets now supports groupby and aggregations, Ray Docker images for multiple CUDA versions, improved Windows support, and a Ray Job Submission server.

3rdGenTasks andActors
11 . 30 . 2021

Deep Dive: Data Ingest in a Third Generation ML Architecture

Distributed libraries allow improved performance by exploiting the full bandwidth of distributed memory, and giving greater programmability. But how does that actually work? What does the code look like?

In this post, we’ll be looking at a concrete...

JK
11 . 29 . 2021

Why I joined Anyscale

Why Jaikumar Ganesh joined Anyscale as Head of Cloud Engineering.

New York City Yellow Taxi ride volumes per location
11 . 23 . 2021

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

What is statistical forecasting and how you can use ARIMA and Prophet on Ray to speed up your forecasting.

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11 . 22 . 2021

Running and Monitoring Distributed ML with Ray and whylogs

Running and monitoring distributed ML systems can be challenging. The need to manage multiple servers, and the fact that those servers emit different logs, means that there can be a lot of overhead involved in scaling up a distributed ML system. Fort...