Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Fraud.net Reviews & Ratings
    4.6 out of 5 stars 56 Ratings
    Company Website for Fraud.net
  • Runpod Reviews & Ratings
    4.2 out of 5 stars 230 Ratings
    Company Website for Runpod
  • Eurekos Reviews & Ratings
    4.8 out of 5 stars 103 Ratings
    Company Website for Eurekos
  • Google Cloud Speech-to-Text Reviews & Ratings
    4.5 out of 5 stars 443 Ratings
    Company Website for Google Cloud Speech-to-Text
  • IONOS Cloud GPU Servers Reviews & Ratings
    4.6 out of 5 stars 45,340 Ratings
    Company Website for IONOS Cloud GPU Servers
  • QuantaStor Reviews & Ratings
    4.8 out of 5 stars 6 Ratings
    Company Website for QuantaStor
  • Gemini Enterprise Agent Platform Reviews & Ratings
    4.3 out of 5 stars 1,161 Ratings
    Company Website for Gemini Enterprise Agent Platform
  • Qloo Reviews & Ratings
    5.0 out of 5 stars 23 Ratings
    Company Website for Qloo
  • Daylight Reviews & Ratings
    4.8 out of 5 stars 15 Ratings
    Company Website for Daylight
  • CoGrader Reviews & Ratings
    4.6 out of 5 stars 73 Ratings
    Company Website for CoGrader

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.

What is Automaton AI?

With Automaton AI's ADVIT, users can easily generate, oversee, and improve high-quality training data along with DNN models, all integrated into one seamless platform. This tool automatically fine-tunes data and readies it for different phases of the computer vision pipeline. It also takes care of data labeling automatically and simplifies in-house data workflows. Users are equipped to manage both structured and unstructured datasets, including video, image, and text formats, while executing automatic functions that enhance data for every step of the deep learning journey. Once the data is meticulously labeled and passes quality checks, users can start training their own models. Effective DNN training involves tweaking hyperparameters like batch size and learning rate to ensure peak performance. Furthermore, the platform facilitates optimization and transfer learning on pre-existing models to boost overall accuracy. After completing training, users can effortlessly deploy their models into a production environment. ADVIT also features model versioning, which enables real-time tracking of development progress and accuracy metrics. By leveraging a pre-trained DNN model for auto-labeling, users can significantly enhance their model's precision, guaranteeing exceptional results throughout the machine learning lifecycle. Ultimately, this all-encompassing solution not only simplifies the development process but also empowers users to achieve outstanding outcomes in their projects, paving the way for innovations in various fields.

Media

Media

Integrations Supported

Caffe
Kubernetes
PyTorch
TensorFlow
Torch

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
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

Automaton AI

Date Founded

2019

Company Location

India

Company Website

automatonai.com

Categories and Features

Deep Learning

Not specified

Neural Network

Not specified

Categories and Features

Data Annotation

Not specified

Data Labeling

Not specified

Deep Learning

Not specified

Image Annotation

Not specified

Machine Learning

Not specified

Neural Network

Not specified

Video Annotation

Not specified

Popular Alternatives

Popular Alternatives