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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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.

Description

This desktop application integrates Jupyter Notebook and Python, allowing for a seamless one-click installation. It features engaging code snippets alongside an AI assistant that enhances coding efficiency, making it an ideal tool for data science endeavors. We have meticulously developed over 100 interactive code recipes tailored for your Data Science projects, which can identify available packages within your current environment. With a single click, you can install any required modules, streamlining your workflow significantly. Users can easily create and manipulate all variables present in their Python session, while these interactive recipes expedite the completion of tasks. The AI Assistant, equipped with knowledge of your active Python session, variables, and modules, is designed to address data challenges using the Python programming language. It offers support for various tasks, including plotting, data loading, data wrangling, and machine learning. If you encounter code issues, simply click the Fix button, and the AI assistant will analyze the problem and suggest a viable solution, making your coding experience smoother and more productive. Additionally, this innovative tool not only simplifies coding but also enhances your learning curve in data science.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Jupyter Notebook
Apache Parquet
Azure Marketplace
Cleanlab
Coiled
DataOps.live
GitHub
HTML
JupyterLab
Markdown
Matplotlib
OpenHexa
Paddle
PayPal
Pickle
Stata
Timbr.ai
Train in Data
XML
scikit-learn

Integrations

Jupyter Notebook
Apache Parquet
Azure Marketplace
Cleanlab
Coiled
DataOps.live
GitHub
HTML
JupyterLab
Markdown
Matplotlib
OpenHexa
Paddle
PayPal
Pickle
Stata
Timbr.ai
Train in Data
XML
scikit-learn

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$20 per month
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

JupyterHub

Founded

2014

Website

github.com/jupyterhub/jupyterhub

Vendor Details

Company Name

MLJAR

Founded

2016

Country

Poland

Website

mljar.com

Product Features

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Machine Learning

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

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