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What is ESF Edge Computing Platform?

Edge Computing facilitates the processing of data closer to its origin, effectively at the periphery of the network. This strategy significantly reduces the communication bandwidth needed between sensors and centralized data centers, as analytics and insights are produced on-site. The IoT service gateway model, which employs powerful enterprise software frameworks and functions as a controller and aggregator within IoT systems, provides an excellent foundation for deploying Edge Computing solutions. Eurotech’s Everyware Software Framework (ESF) is recognized as a commercial, enterprise-grade variant of Eclipse Kura, an open-source platform tailored for Edge Computing in IoT gateways. Distributed and supported by Eurotech, ESF enhances Eclipse Kura with advanced features that improve security, diagnostics, provisioning, remote access, and seamless integration with Everyware Cloud, Eurotech's IoT Integration Platform. Moreover, this integration not only amplifies the effectiveness of IoT deployments but also promotes improved efficiency in managing data flows, ultimately leading to a more responsive and streamlined operational environment.

What is Deeplearning4j?

DL4J utilizes cutting-edge distributed computing technologies like Apache Spark and Hadoop to significantly improve training speed. When combined with multiple GPUs, it achieves performance levels that rival those of Caffe. Completely open-source and licensed under Apache 2.0, the libraries benefit from active contributions from both the developer community and the Konduit team. Developed in Java, Deeplearning4j can work seamlessly with any language that operates on the JVM, which includes Scala, Clojure, and Kotlin. The underlying computations are performed in C, C++, and CUDA, while Keras serves as the Python API. Eclipse Deeplearning4j is recognized as the first commercial-grade, open-source, distributed deep-learning library specifically designed for Java and Scala applications. By connecting with Hadoop and Apache Spark, DL4J effectively brings artificial intelligence capabilities into the business realm, enabling operations across distributed CPUs and GPUs. Training a deep-learning network requires careful tuning of numerous parameters, and efforts have been made to elucidate these configurations, making Deeplearning4j a flexible DIY tool for developers working with Java, Scala, Clojure, and Kotlin. With its powerful framework, DL4J not only streamlines the deep learning experience but also encourages advancements in machine learning across a wide range of sectors, ultimately paving the way for innovative solutions. This evolution in deep learning technology stands as a testament to the potential applications that can be harnessed in various fields.

Media

Media

Integrations Supported

Apache Spark
Hadoop

Integrations Supported

Apache Spark
Hadoop

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

Eclipse Kura

Company Website

esf.eurotech.com/v5.0.0/docs/edge-computing-platform

Company Facts

Organization Name

Deeplearning4j

Date Founded

2019

Company Location

Japan

Company Website

deeplearning4j.org

Categories and Features

IoT

Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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