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Description

Amazon SageMaker Canvas democratizes access to machine learning by equipping business analysts with an intuitive visual interface that enables them to independently create precise ML predictions without needing prior ML knowledge or coding skills. This user-friendly point-and-click interface facilitates the connection, preparation, analysis, and exploration of data, simplifying the process of constructing ML models and producing reliable predictions. Users can effortlessly build ML models to conduct what-if scenarios and generate both individual and bulk predictions with minimal effort. The platform enhances teamwork between business analysts and data scientists, allowing for the seamless sharing, reviewing, and updating of ML models across different tools. Additionally, users can import ML models from various sources and obtain predictions directly within Amazon SageMaker Canvas. With this tool, you can draw data from diverse origins, specify the outcomes you wish to forecast, and automatically prepare as well as examine your data, enabling a swift and straightforward model-building experience. Ultimately, this capability allows users to analyze their models and yield accurate predictions, fostering a more data-driven decision-making culture across organizations.

Description

Utilize open-source machine learning tools and data visualization techniques to create dynamic data analysis workflows in a visual format, supported by a broad and varied collection of resources. Conduct straightforward data assessments accompanied by insightful visual representations, and investigate statistical distributions through box plots and scatter plots; for more complex inquiries, utilize decision trees, hierarchical clustering, heatmaps, multidimensional scaling, and linear projections. Even intricate multidimensional datasets can be effectively represented in 2D, particularly through smart attribute selection and ranking methods. Engage in interactive data exploration for swift qualitative analysis, enhanced by clear visual displays. The user-friendly graphic interface enables a focus on exploratory data analysis rather than programming, while intelligent defaults facilitate quick prototyping of data workflows. Simply position widgets on your canvas, link them together, import your datasets, and extract valuable insights! When it comes to teaching data mining concepts, we prefer to demonstrate rather than merely describe, and Orange excels in making this approach effective and engaging. The platform not only simplifies the process but also enriches the learning experience for users at all levels.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Amazon SageMaker Unified Studio Yes 
Amazon Web Services (AWS) Yes 

Integrations

Amazon SageMaker No 
Amazon SageMaker Unified Studio No 
Amazon Web Services (AWS) No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
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 No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux No 
Chromebook No 

Customer Support

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

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/ai/canvas/

Vendor Details

Company Name

University of Ljubljana

Country

Slovenia

Website

orange.biolab.si

Product Features

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 

Product Features

Data Mining

Data Extraction No 
Data Visualization No 
Fraud Detection No 
Linked Data Management No 
Machine Learning No 
Predictive Modeling No 
Semantic Search No 
Statistical Analysis No 
Text Mining No 

Data Visualization

Analytics No 
Content Management No 
Dashboard Creation Yes 
Filtered Views No 
OLAP No 
Relational Display No 
Simulation Models No 
Visual Discovery Yes 

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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