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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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace
Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Integrations

AWS Marketplace
Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

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
Compliance Management
Dashboard
Disaster Recovery
IT Risk Management
Incident Management
Operational Risk Management
Risk Assessment
Safety Management
Vendor Management

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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