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Description

The Apache Lucene™ initiative is dedicated to creating open-source search technology. This initiative not only offers a fundamental library known as Lucene™ core but also includes PyLucene, which serves as a Python interface for Lucene. Lucene Core functions as a Java library that delivers robust features for indexing and searching, including capabilities for spellchecking, hit highlighting, and sophisticated analysis/tokenization. The PyLucene project enhances accessibility by allowing developers to utilize Lucene Core through Python. Backing this initiative is the Apache Software Foundation, which supports a variety of open-source software endeavors. Notably, Apache Lucene is made available under a license that is favorable for commercial use. It has established itself as a benchmark for search and indexing efficiency. Furthermore, Lucene is the foundational search engine for both Apache Solr™ and Elasticsearch™, which are widely used in various applications. From mobile platforms to major websites like Twitter, Apple, and Wikipedia, our core algorithms, together with the Solr search server, enable a multitude of applications globally. Ultimately, the objective of Apache Lucene is to deliver exceptional search capabilities that meet the needs of diverse users. Its continuous development reflects the commitment to innovation in search technology.

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.

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 Solr
Apache Spark
Apache Usergrid
Elasticsearch
Hadoop
Java
Kubernetes
MapReduce
Python
R
Scala

Integrations

Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Solr
Apache Spark
Apache Usergrid
Elasticsearch
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

1999

Country

United States

Website

lucene.apache.org

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Product Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

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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