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What is Fabric for Deep Learning (FfDL)?

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.

What is ElysianNxt?

ElysianNxt's .NXT Platform is an advanced risk management solution meticulously designed to prepare organizations for triumph in the rapidly evolving regulatory environment. Emphasizing innovation, it leverages cutting-edge technologies such as data streaming, targeted scalability, microservices, polyglot architectures, and open-source integration to transform traditional batch processes into near real-time operations. This comprehensive risk management system not only offers robust scenario analysis capabilities but can be deployed on-premise or via a SaaS model, ensuring exceptional operational resilience alongside real-time data processing. The microservices architecture guarantees both high availability and fault tolerance, while its database-agnostic design allows for seamless adaptation across diverse technological landscapes. Additionally, the platform boasts an integrated simulation framework that enables users to perform stress tests on all risk categories without the need for separate testing environments, supporting an unlimited number of simulations. The combination of these sophisticated features firmly establishes the .NXT Platform as an essential resource for organizations aiming to effectively navigate the complexities of regulatory compliance, thereby enhancing their strategic decision-making processes. Organizations that utilize this platform can expect to achieve a competitive edge in their respective markets through superior risk management capabilities.

Media

Media

Integrations Supported

Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Integrations Supported

AWS Marketplace

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

Company Facts

Organization Name

ElysianNxt

Date Founded

2017

Company Location

Belgium

Company Website

www.elysiannxt.com/the-nxt-platform/

Categories and Features

Deep Learning

Not specified

Neural Network

Not specified

Categories and Features

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