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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
Integrations
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
Integrations
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
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