Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
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