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ease
features
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Description

MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.

Description

Protégé benefits from a robust network of users from academia, government, and industry, who utilize it to create knowledge-driven solutions across various fields such as biomedicine, e-commerce, and organizational modeling. Its versatile plug-in architecture allows for the development of both straightforward and intricate ontology-based applications. Developers have the capability to connect Protégé's outputs with rule systems or other problem-solving tools, enabling the creation of a diverse array of intelligent systems. Crucially, the dedicated Stanford team, alongside the extensive Protégé community, is readily available to provide assistance. This community actively engages by answering inquiries, contributing to documentation, and developing plug-ins. Furthermore, Protégé's foundation in Java enhances its extensibility, while its plug-and-play environment ensures it serves as a flexible platform for quick prototyping and application development, paving the way for innovative projects and solutions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
Python
R
Scala

Integrations

Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
Python
R
Scala

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

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Vendor Details

Company Name

Center for Biomedical Informatics Research

Country

United States

Website

protege.stanford.edu/

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

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