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Ratings and Reviews 0 Ratings
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What is Fabric for Deep Learning (FfDL)?
Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.
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The ABEJA platform signifies a revolutionary leap in artificial intelligence, combining cutting-edge innovations like IoT, Big Data, and Deep Learning technologies. Back in 2013, data circulation stood at 4.4 zettabytes, yet forecasts indicated an astonishing rise to 44 zettabytes by 2020, leading to critical inquiries about how to effectively collect and utilize this immense array of information. In addition, it raises vital considerations regarding the strategies we might adopt to derive fresh insights and value from this wealth of data. The ABEJA platform emerges as a frontrunner in AI, tackling the increasingly intricate technological challenges of tomorrow while improving the utilization of varied data sources. It boasts advanced image analysis functions driven by Deep Learning and can swiftly handle extensive datasets thanks to its innovative decentralized processing framework. Alongside this, it leverages Machine Learning and Deep Learning methodologies to sift through the amassed data, while also enabling effortless delivery of analytical outcomes via its API, rendering it an essential asset for organizations aiming to innovate and succeed in an era dominated by data. Moreover, as it continues to evolve with technological progress, ABEJA is poised to further expand the horizons of AI applications across diverse sectors. The platform not only showcases the capabilities of artificial intelligence but also inspires confidence in its potential to transform industries fundamentally.
Integrations Supported
Caffe
Kubernetes
PyTorch
TensorFlow
Torch
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
IBM
Date Founded
1911
Company Location
United States
Company Website
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/
Company Facts
Organization Name
ABEJA
Date Founded
2012
Company Location
Japan
Company Website
abejainc.com/platform/en/
Categories and Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Categories and Features
Big Data
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
