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ease
features
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support

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Description

ElysianNxt's .NXT Platform represents a cutting-edge risk management solution specifically crafted to equip organizations for success in the fast-changing regulatory landscape. Developed with a focus on innovation, it harnesses state-of-the-art technologies like data streaming, targeted scalability, microservices, polyglot architectures, and open source integration to revolutionize conventional batch processes into operations that approach real-time efficiency. This unified risk management system provides thorough scenario analysis capabilities and can be implemented either on-premise or through a SaaS model, delivering unmatched operational resilience alongside real-time data processing. The microservices framework guarantees high availability and fault tolerance, while its database-agnostic nature ensures adaptability across various technological environments. Furthermore, the platform features a built-in simulation framework that allows users to conduct stress tests on all risk categories seamlessly, eliminating the need for distinct testing environments and facilitating an infinite number of simulations. By combining these advanced features, the .NXT Platform positions itself as an indispensable tool for organizations striving to navigate complex regulatory demands effectively.

Description

Deep learning frameworks like TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have significantly enhanced the accessibility of deep learning by simplifying the design, training, and application of deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) offers a standardized method for deploying these deep-learning frameworks as a service on Kubernetes, ensuring smooth operation. The architecture of FfDL is built on microservices, which minimizes the interdependence between components, promotes simplicity, and maintains a stateless nature for each component. This design choice also helps to isolate failures, allowing for independent development, testing, deployment, scaling, and upgrading of each element. By harnessing the capabilities of Kubernetes, FfDL delivers a highly scalable, resilient, and fault-tolerant environment for deep learning tasks. Additionally, the platform incorporates a distribution and orchestration layer that enables efficient learning from large datasets across multiple compute nodes within a manageable timeframe. This comprehensive approach ensures that deep learning projects can be executed with both efficiency and reliability.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace Yes 
Caffe No 
Kubernetes No 
PyTorch No 
TensorFlow No 
Torch No 

Integrations

AWS Marketplace No 
Caffe Yes 
Kubernetes Yes 
PyTorch Yes 
TensorFlow Yes 
Torch Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

ElysianNxt

Founded

2017

Country

Belgium

Website

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

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

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

Product Features

Integrated Risk Management

Audit Management No 
Compliance Management No 
Dashboard No 
Disaster Recovery No 
IT Risk Management No 
Incident Management No 
Operational Risk Management No 
Risk Assessment No 
Safety Management No 
Vendor Management No 

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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