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Average Ratings 0 Ratings
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.
Description
W2Bill Payments efficiently manages fund requests while monitoring their defined time-to-live, addressing both acceptance and rejection responses, and aligning the data with the systems of the infrastructure, independent of their roles. It supports and facilitates real-time payment requests, cancellations, confirmations, and rejections. Additionally, it processes and creates files offline, whether they are incoming from or outgoing to financial institutions, ensuring seamless operations. The system employs real-time communication models that encompass both synchronous and asynchronous methods. Furthermore, it introduces new payment channels that utilize various protocols. Fault tolerance is achieved through component clustering, eliminating any central point of failure. Inter-component communication is facilitated through messaging, which guarantees at-least-once delivery assurance. The competition is intensified by the presence of stored credentials, driving fintech startups, credit card providers, and other payment handlers to strive for a seamless and instantaneous payment experience. As a result, the landscape of payment processing continues to evolve rapidly.
API Access
Has API
API Access
Has API
Integrations
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
IBM
Founded
1911
Country
United States
Website
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/
Vendor Details
Company Name
CMAS Systems Consultants
Founded
2001
Country
Portugal
Website
www.w2bill.com/payments/
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Payment Processing
ACH Check Transactions
Bitcoin Compatible
Debit Card Support
Gift Card Management
Mobile Payments
Online Payments
POS Transactions
Receipt Printing
Recurring Billing
Signature Capture