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Average Ratings 0 Ratings

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ease
features
design
support

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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

Spark offers a three-dimensional perspective of your application's interface along with the capability to adjust view settings dynamically during runtime, enabling you to design exceptional applications. If your app relies on notifications, Spark's notification monitor tracks each NSNotification as it is dispatched, providing a comprehensive stack trace, a detailed list of recipients, the methods invoked, and additional relevant information. This feature allows for a quick understanding of your app's architecture while enhancing debugging efficiency. By connecting your application to the Spark Inspector, you place your app's interface in the spotlight, with real-time updates reflecting your interactions. We keep track of every alteration within your app's view hierarchy, ensuring you remain informed about ongoing changes. The visual representation of your app in Spark is not only aesthetically pleasing but also fully customizable. You have the ability to alter nearly every aspect of your views, from class-level properties to CALayer transformations, and upon making any changes, Spark triggers a method within your app to directly implement that adjustment. This seamless integration fosters a more intuitive development experience, allowing for rapid iteration and refinement.

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
Xcode

Integrations

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

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

$49.99 one-time payment
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

Spark Inspector

Website

sparkinspector.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

Alternatives

Alternatives

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