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features
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support

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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

Kraken caters to a wide range of users, from analysts to data scientists, by providing a user-friendly, no-code automated machine learning platform. It is designed to streamline and automate various data science processes, including data preparation, cleaning, algorithm selection, model training, and deployment. With a focus on making these tasks accessible, Kraken is particularly beneficial for analysts and engineers who may have some experience in data analysis. The platform’s intuitive, no-code interface and integrated SONAR© training empower users to evolve into citizen data scientists effortlessly. For data scientists, advanced functionalities enhance productivity and efficiency. Whether your routine involves using Excel or flat files for reporting or conducting ad-hoc analysis, Kraken simplifies the model-building process with features like drag-and-drop CSV uploads and an Amazon S3 connector. Additionally, the Data Connectors in Kraken enable seamless integration with various data warehouses, business intelligence tools, and cloud storage solutions, ensuring that users can work with their preferred data sources effortlessly. This versatility makes Kraken an indispensable tool for anyone looking to leverage machine learning without requiring extensive coding knowledge.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 No 
Apache Spark Yes 
Domo No 
Google Cloud BigQuery No 
Hadoop Yes 
Kryptos No 
Looker No 
MySQL No 
Pulsar Finance No 
Qlik Sense No 
SQL Server No 
SimpleSwap No 
Snowflake No 
Tableau No 
Tradoshi No 

Integrations

Amazon S3 Yes 
Apache Spark No 
Domo Yes 
Google Cloud BigQuery Yes 
Hadoop No 
Kryptos Yes 
Looker Yes 
MySQL Yes 
Pulsar Finance Yes 
Qlik Sense Yes 
SQL Server Yes 
SimpleSwap Yes 
Snowflake Yes 
Tableau Yes 
Tradoshi Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$100 per month
Free Trial Yes 
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 No 
Live Rep (24/7) No 
Online Support No 

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) No 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Country

United States

Website

mahout.apache.org

Vendor Details

Company Name

Big Squid

Founded

2009

Country

United States

Website

www.bigsquid.com/kraken-platform

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

Big Data

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

Machine Learning

Deep Learning Yes 
ML Algorithm Library Yes 
Model Training Yes 
Natural Language Processing (NLP) No 
Predictive Modeling Yes 
Statistical / Mathematical Tools Yes 
Templates Yes 
Visualization Yes 

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