Average Ratings 2 Ratings

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Average Ratings 0 Ratings

Total
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Description

The Jupyter Notebook is a web-based open-source tool that enables users to create and distribute documents featuring live code, visualizations, equations, and written explanations. Its applications are diverse and encompass tasks such as data cleaning and transformation, statistical modeling, numerical simulations, data visualization, machine learning, among others, showcasing its versatility in various fields. Additionally, it serves as an excellent platform for collaboration and sharing insights within the data science community.

Description

A data science platform designed to enhance productivity offers unmatched features that facilitate the development and assessment of superior machine learning (ML) models. By leveraging enterprise-trusted data swiftly, businesses can achieve greater flexibility and meet their data-driven goals through simpler deployment of ML models. Cloud-based solutions enable organizations to uncover valuable business insights efficiently. The journey of constructing a machine learning model is inherently iterative, and this ebook meticulously outlines the stages involved in its creation. Readers can engage with notebooks to either build or evaluate various machine learning algorithms. Experimenting with AutoML can yield impressive data science outcomes, allowing users to create high-quality models with greater speed and ease. Moreover, automated machine learning processes quickly analyze datasets, recommending the most effective data features and algorithms while also fine-tuning models and clarifying their results. This comprehensive approach ensures that businesses can harness the full potential of their data, driving innovation and informed decision-making.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AnzoGraph DB Yes 
AutoKeras Yes 
Crowdin Yes 
Fuzzball Yes 
Google Cloud Datalab Yes 
Gradient Yes 
LSEG Workspace Yes 
Lepton Yes 
MinusX Yes 
Modern DataOS Yes 
OpenHexa Yes 
Pop!_OS Yes 
Racket Yes 
Scout Yes 
Tokern Yes 
neptune.ai Yes 
packet.ai Yes 
runcell.dev Yes 

Integrations

AnzoGraph DB No 
AutoKeras No 
Crowdin No 
Fuzzball No 
Google Cloud Datalab No 
Gradient No 
LSEG Workspace No 
Lepton No 
MinusX No 
Modern DataOS No 
OpenHexa No 
Pop!_OS No 
Racket No 
Scout No 
Tokern No 
neptune.ai No 
packet.ai No 
runcell.dev No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version 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 

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) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
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

Project Jupyter

Founded

2014

Website

jupyter.org

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/data-science/

Product Features

Application Development

Access Controls/Permissions No 
Code Assistance No 
Code Refactoring No 
Collaboration Tools No 
Compatibility Testing No 
Data Modeling No 
Debugging No 
Deployment Management No 
Graphical User Interface No 
Mobile Development No 
No-Code No 
Reporting/Analytics No 
Software Development No 
Source Control No 
Testing Management No 
Version Control No 
Web App Development No 

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports No 

Product Features

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports 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 

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