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What is Ray?

You can start developing on your laptop and then effortlessly scale your Python code across numerous GPUs in the cloud. Ray transforms conventional Python concepts into a distributed framework, allowing for the straightforward parallelization of serial applications with minimal code modifications. With a robust ecosystem of distributed libraries, you can efficiently manage compute-intensive machine learning tasks, including model serving, deep learning, and hyperparameter optimization. Scaling existing workloads is straightforward, as demonstrated by how Pytorch can be easily integrated with Ray. Utilizing Ray Tune and Ray Serve, which are built-in Ray libraries, simplifies the process of scaling even the most intricate machine learning tasks, such as hyperparameter tuning, training deep learning models, and implementing reinforcement learning. You can initiate distributed hyperparameter tuning with just ten lines of code, making it accessible even for newcomers. While creating distributed applications can be challenging, Ray excels in the realm of distributed execution, providing the tools and support necessary to streamline this complex process. Thus, developers can focus more on innovation and less on infrastructure.

What is MLBox?

MLBox is a sophisticated Python library tailored for Automated Machine Learning, providing a multitude of features such as swift data ingestion, effective distributed preprocessing, thorough data cleansing, strong feature selection, and precise leak detection. It stands out with its capability for hyper-parameter optimization in complex, high-dimensional environments and incorporates state-of-the-art predictive models for both classification and regression, including techniques like Deep Learning, Stacking, and LightGBM, along with tools for interpreting model predictions. The main MLBox package is organized into three distinct sub-packages: preprocessing, optimization, and prediction, each designed to fulfill specific functions: the preprocessing module is dedicated to data ingestion and preparation, the optimization module experiments with and refines various learners, and the prediction module is responsible for making predictions on test datasets. This structured approach guarantees a smooth workflow for machine learning professionals, enhancing their productivity. In essence, MLBox streamlines the machine learning journey, rendering it both user-friendly and efficient for those seeking to leverage its capabilities.

Media

Media

Integrations Supported

Python
Amazon EC2 Trn2 Instances
Amazon EKS
Amazon Web Services (AWS)
Anyscale
Azure Kubernetes Service (AKS)
Dask
Databricks
Feast
Flyte
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Snowflake
TensorFlow
Union Cloud

Integrations Supported

Python
GitHub

API Availability

Has API

API Availability

Pricing Information

Free
Open source. Consumption-based.
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Company Facts

Organization Name

Axel ARONIO DE ROMBLAY

Date Founded

2017

Company Website

mlbox.readthedocs.io/en/latest/

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

Machine Learning

Not specified

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