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ease
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

The SensiML Analytics Toolkit enables the swift development of smart IoT sensor devices while simplifying the complexities of data science. It focuses on creating compact algorithms designed to run on small IoT endpoints instead of relying on cloud processing. By gathering precise, traceable, and version-controlled datasets, it enhances data integrity. The toolkit employs advanced AutoML code generation to facilitate the rapid creation of autonomous device code. Users can select their preferred interface and level of AI expertise while maintaining full oversight of all algorithm components. It also supports the development of edge tuning models that adapt behavior based on incoming data over time. The SensiML Analytics Toolkit automates every step necessary for crafting optimized AI recognition code for IoT sensors. Utilizing an expanding library of sophisticated machine learning and AI algorithms, the overall workflow produces code capable of learning from new data, whether during development or after deployment. Moreover, non-invasive applications for rapid disease screening that intelligently classify multiple bio-sensing inputs serve as essential tools for aiding healthcare decision-making processes. This capability positions the toolkit as an invaluable resource in both tech and healthcare sectors.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apache Spark
Hadoop

Integrations

Apache Spark
Hadoop

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
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

Apache Software Foundation

Country

United States

Website

mahout.apache.org

Vendor Details

Company Name

SensiML

Founded

2017

Country

United States

Website

sensiml.com

Product Features

Machine Learning

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

Product Features

Machine Learning

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

Alternatives

Alternatives

MXNet Reviews

MXNet

The Apache Software Foundation