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features
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support

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Write a Review

Description

Apache Mahout is an advanced and adaptable machine learning library that excels in processing distributed datasets efficiently. It encompasses a wide array of algorithms suitable for tasks such as classification, clustering, recommendation, and pattern mining. By integrating seamlessly with the Apache Hadoop ecosystem, Mahout utilizes MapReduce and Spark to facilitate the handling of extensive datasets. This library functions as a distributed linear algebra framework, along with a mathematically expressive Scala domain-specific language, which empowers mathematicians, statisticians, and data scientists to swiftly develop their own algorithms. While Apache Spark is the preferred built-in distributed backend, Mahout also allows for integration with other distributed systems. Matrix computations play a crucial role across numerous scientific and engineering disciplines, especially in machine learning, computer vision, and data analysis. Thus, Apache Mahout is specifically engineered to support large-scale data processing by harnessing the capabilities of both Hadoop and Spark, making it an essential tool for modern data-driven applications.

Description

The Rinalogy Classification API offers a flexible machine learning solution that seamlessly integrates into your existing application while allowing you to operate within your own infrastructure. In contrast to traditional cloud-based machine learning APIs that necessitate data transfer and operate in an external environment, Rinalogy allows for deployment within your IT framework, ensuring data security and compliance as it works behind your firewall. This API utilizes Exhaustive Sequential Classification, systematically applying models to every document within a dataset. The models generated can be enhanced with additional training data or leveraged for predicting outcomes on new documents at a later time. With its ability to scale through cluster deployment, you can modify the number of workers based on your current workload needs. Furthermore, the Rinalogy API empowers client applications by incorporating features such as text classification, enhanced search capabilities, and personalized recommendations, providing a comprehensive toolkit for data-driven decision-making. This versatility makes it an appealing choice for organizations aiming to optimize their machine learning processes while maintaining control over their data.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
Hadoop Yes 

Integrations

Apache Spark No 
Hadoop No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

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

Apache Software Foundation

Country

United States

Website

mahout.apache.org

Vendor Details

Company Name

RINA Systems

Country

United States

Website

www.rinasystems.com/product/rinalogy-api.html

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

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

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