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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Apache Ivy™ serves as a widely-used dependency manager that emphasizes both flexibility and ease of use. Discover its distinct enterprise capabilities, user feedback, and the ways it can enhance your build process! Ivy operates as a tool designed for the management of project dependencies, which includes recording, tracking, resolving, and reporting. It is not confined to any specific methodology or framework, allowing it to be highly adaptable to various dependency management and build workflows. Although it can function independently, Ivy is particularly effective in conjunction with Apache Ant, offering a variety of robust Ant tasks that range from resolving dependencies to generating reports and facilitating publication. Among its many powerful attributes, users often highlight its flexibility, seamless integration with Ant, and an efficient engine for managing transitive dependencies. Additionally, Ivy is an open-source tool, distributed under a permissive Apache License, making it accessible for a wide audience. This combination of features positions Ivy as a valuable asset for developers seeking to streamline their dependency management processes.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon EC2 No 
Apache Ant Yes 
Apache Cassandra No 
Apache HBase No 
Apache Hive No 
Apache Mesos No 
Apache Spark No 
Hadoop No 
JFrog Yes 
Java No 
Kubernetes No 
MapReduce No 
Perforce TeamHub Yes 
Python No 
R No 
Scala No 

Integrations

Amazon EC2 Yes 
Apache Ant No 
Apache Cassandra Yes 
Apache HBase Yes 
Apache Hive Yes 
Apache Mesos Yes 
Apache Spark Yes 
Hadoop Yes 
JFrog No 
Java Yes 
Kubernetes Yes 
MapReduce Yes 
Perforce TeamHub No 
Python Yes 
R Yes 
Scala Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 No 
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 No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Apache Software Foundation

Country

United States

Website

ant.apache.org/ivy/

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Product Features

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 

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