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ease
features
design
support

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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

Screenshots View All

Screenshots View All

Integrations

Caffe
Kubernetes
PyTorch
TensorFlow
Torch

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

Alternatives

Alternatives

E-Delivery Reviews

E-Delivery

AppEmporio