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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Hopsworks is a comprehensive open-source platform designed to facilitate the creation and management of scalable Machine Learning (ML) pipelines, featuring the industry's pioneering Feature Store for ML. Users can effortlessly transition from data analysis and model creation in Python, utilizing Jupyter notebooks and conda, to executing robust, production-ready ML pipelines without needing to acquire knowledge about managing a Kubernetes cluster. The platform is capable of ingesting data from a variety of sources, whether they reside in the cloud, on-premise, within IoT networks, or stem from your Industry 4.0 initiatives. You have the flexibility to deploy Hopsworks either on your own infrastructure or via your chosen cloud provider, ensuring a consistent user experience regardless of the deployment environment, be it in the cloud or a highly secure air-gapped setup. Moreover, Hopsworks allows you to customize alerts for various events triggered throughout the ingestion process, enhancing your workflow efficiency. This makes it an ideal choice for teams looking to streamline their ML operations while maintaining control over their data environments.

Description

JupyterHub allows users to establish a multi-user environment that can spawn, manage, and proxy several instances of the individual Jupyter notebook server. Developed by Project Jupyter, JupyterHub is designed to cater to numerous users simultaneously. This platform can provide notebook servers for a variety of purposes, including educational environments for students, corporate data science teams, collaborative scientific research, or groups utilizing high-performance computing resources. It is important to note that JupyterHub does not officially support Windows operating systems. While it might be possible to run JupyterHub on Windows by utilizing compatible Spawners and Authenticators, the default configurations are not designed for this platform. Furthermore, any bugs reported on Windows will not be addressed, and the testing framework does not operate on Windows systems. Although minor patches to resolve basic Windows compatibility issues may be considered, they are rare. For users on Windows, it is advisable to run JupyterHub within a Docker container or a Linux virtual machine to ensure optimal performance and compatibility. This approach not only enhances functionality but also simplifies the installation process for Windows users.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon EC2 Yes 
Amazon Web Services (AWS) Yes 
Azure Marketplace No 
Cleanlab No 
Coiled No 
DataOps.live No 
IBM watsonx.data Yes 
JetBrains DataSpell No 
Jupyter Notebook No 
JupyterLab No 
NeevCloud No 
Onehouse Yes 
OpenHexa No 
Quantinuum Nexus No 
Timbr.ai No 
Train in Data No 
Vast.ai No 
Wizata No 

Integrations

Amazon EC2 No 
Amazon Web Services (AWS) No 
Azure Marketplace Yes 
Cleanlab Yes 
Coiled Yes 
DataOps.live Yes 
IBM watsonx.data No 
JetBrains DataSpell Yes 
Jupyter Notebook Yes 
JupyterLab Yes 
NeevCloud Yes 
Onehouse No 
OpenHexa Yes 
Quantinuum Nexus Yes 
Timbr.ai Yes 
Train in Data Yes 
Vast.ai Yes 
Wizata Yes 

Pricing Details

$1 per month
Free Trial Yes 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Logical Clocks

Founded

2016

Country

Sweden

Website

www.logicalclocks.com/hopsworks

Vendor Details

Company Name

JupyterHub

Founded

2014

Website

github.com/jupyterhub/jupyterhub

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare Yes 
For Sales No 
For eCommerce Yes 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics Yes 
Process/Workflow Automation Yes 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Big Data

Collaboration Yes 
Data Blends No 
Data Cleansing Yes 
Data Mining Yes 
Data Visualization Yes 
Data Warehousing Yes 
High Volume Processing Yes 
No-Code Sandbox No 
Predictive Analytics No 
Templates Yes 

Data Analysis

Data Discovery Yes 
Data Visualization Yes 
High Volume Processing Yes 
Predictive Analytics No 
Regression Analysis Yes 
Sentiment Analysis No 
Statistical Modeling No 
Text Analytics No 

Data Management

Customer Data Yes 
Data Analysis Yes 
Data Capture No 
Data Integration Yes 
Data Migration Yes 
Data Quality Control Yes 
Data Security Yes 
Information Governance No 
Master Data Management Yes 
Match & Merge No 

Machine Learning

Deep Learning Yes 
ML Algorithm Library Yes 
Model Training Yes 
Natural Language Processing (NLP) Yes 
Predictive Modeling Yes 
Statistical / Mathematical Tools Yes 
Templates Yes 
Visualization Yes 

Product Features

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