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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 Peltarion?

The Peltarion Platform serves as an intuitive low-code interface tailored for deep learning, enabling users to rapidly develop AI solutions that are commercially viable. It streamlines every stage of the deep learning model lifecycle, from initial creation to fine-tuning and deployment, all within a single cohesive environment. This all-encompassing platform offers capabilities for managing everything from data ingestion to model deployment effortlessly. Major institutions such as NASA, Tesla, Dell, and Harvard have utilized both the Peltarion Platform and its predecessor to tackle intricate problems. Users have the flexibility to build their own AI models or select from a range of pre-built options, all accessible via a user-friendly drag-and-drop interface that incorporates the latest innovations. Complete oversight of the development process—from model construction and training to refinement and implementation—is provided, ensuring a smooth integration of AI solutions. By harnessing the potential of AI through this platform, organizations can realize substantial benefits. To support those unfamiliar with AI concepts, the Faster AI course offers essential training; completing its seven brief modules equips participants with the skills needed to design and modify their own AI models on the Peltarion platform, nurturing a new wave of AI enthusiasts. This program not only broadens individual expertise but also plays a significant role in promoting the widespread adoption of AI technologies across various sectors. Ultimately, the Peltarion Platform stands as a vital resource for both seasoned professionals and newcomers alike, fostering innovation and efficiency in AI development.

Media

Media

Integrations Supported

Google Cloud Platform
Amazon EKS
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Dask
Databricks
Feast
Flyte
Google Kubernetes Engine (GKE)
Kubernetes
PyTorch
Python
TensorFlow
Union Cloud
io.net

Integrations Supported

Google Cloud Platform
Azure Synapse Analytics
Bubble
Microsoft Power Apps
Stackreaction

API Availability

Has API

API Availability

Pricing Information

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

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

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
Webinars

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Company Facts

Organization Name

Peltarion

Date Founded

2004

Company Location

Sweden

Company Website

peltarion.com

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

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