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ease
features
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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

Paradise employs advanced unsupervised machine learning alongside supervised deep learning techniques to enhance data interpretation and derive deeper insights. It creates specific attributes that help in extracting significant geological information, which can then be utilized for machine learning analyses. The system identifies attributes that exhibit the most variation and influence within a geological context. Additionally, it visualizes neural classes and their corresponding colors from Stratigraphic Analysis, which reveal the spatial distribution of different facies. Faults are detected automatically through a combination of deep learning and machine learning methods. Furthermore, it allows for a comparison between machine learning classification outcomes and other seismic attributes against traditional high-quality logs. Lastly, it generates both geometric and spectral decomposition attributes across a cluster of computing nodes, achieving results in a fraction of the time it would take on a single machine. This efficiency enhances the overall productivity of geoscientific research and analysis.

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

Geophysical Insights

Founded

2009

Country

United States

Website

www.geoinsights.com/products/

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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