Average Ratings 0 Ratings
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
Segmind simplifies access to extensive computing resources, making it ideal for executing demanding tasks like deep learning training and various intricate processing jobs. It offers environments that require no setup within minutes, allowing for easy collaboration among team members. Additionally, Segmind's MLOps platform supports comprehensive management of deep learning projects, featuring built-in data storage and tools for tracking experiments. Recognizing that machine learning engineers often lack expertise in cloud infrastructure, Segmind takes on the complexities of cloud management, enabling teams to concentrate on their strengths and enhance model development efficiency. As training machine learning and deep learning models can be time-consuming and costly, Segmind allows for effortless scaling of computational power while potentially cutting costs by up to 70% through managed spot instances. Furthermore, today's ML managers often struggle to maintain an overview of ongoing ML development activities and associated expenses, highlighting the need for robust management solutions in the field. By addressing these challenges, Segmind empowers teams to achieve their goals more effectively.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Caffe
Yes
Hyperbolic
No
Kubernetes
Yes
PyTorch
Yes
TensorFlow
Yes
Torch
Yes
Integrations
Caffe
No
Hyperbolic
Yes
Kubernetes
No
PyTorch
No
TensorFlow
No
Torch
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$5
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
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
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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
Segmind
Founded
2020
Country
India
Website
Segmind.com
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
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
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No