Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The continuous integration tool known as Apache Gump was the inaugural project created by the Apache Software Foundation. Developed in Python, it offers comprehensive support for build tools like Apache Ant and Apache Maven (versions 1.x to 3.x). What sets Gump apart is its capability to build and compile software against the most recent development iterations of various projects. This functionality enables Gump to identify potentially breaking changes to software just hours after they are committed to the version control system. Upon detecting such changes, it promptly alerts the project team, providing access to more extensive reports online for further investigation. While you can install and operate Gump on your personal computer to manage your own projects, it is predominantly recognized for its role in building numerous Apache projects and their respective dependencies. To facilitate this, the Gump initiative maintains a dedicated server specifically for its operations, ensuring efficiency and reliability in continuous integration processes. Gump's commitment to early detection of issues greatly enhances the overall software development cycle.
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
Integrations
Amazon EC2
No
Apache Cassandra
No
Apache HBase
No
Apache Hive
No
Apache Mesos
No
Apache Spark
No
Hadoop
No
Java
No
Kubernetes
No
MapReduce
No
Integrations
Amazon EC2
Yes
Apache Cassandra
Yes
Apache HBase
Yes
Apache Hive
Yes
Apache Mesos
Yes
Apache Spark
Yes
Hadoop
Yes
Java
Yes
Kubernetes
Yes
MapReduce
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
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
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
United States
Website
gump.apache.org
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Product Features
Continuous Integration
Build Log
No
Change Management
Yes
Configuration Management
No
Continuous Delivery
No
Continuous Deployment
No
Debugging
No
Permission Management
No
Quality Assurance Management
No
Testing Management
No
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