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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
The Rinalogy Classification API offers a flexible machine learning solution that seamlessly integrates into your existing application while allowing you to operate within your own infrastructure. In contrast to traditional cloud-based machine learning APIs that necessitate data transfer and operate in an external environment, Rinalogy allows for deployment within your IT framework, ensuring data security and compliance as it works behind your firewall. This API utilizes Exhaustive Sequential Classification, systematically applying models to every document within a dataset. The models generated can be enhanced with additional training data or leveraged for predicting outcomes on new documents at a later time. With its ability to scale through cluster deployment, you can modify the number of workers based on your current workload needs. Furthermore, the Rinalogy API empowers client applications by incorporating features such as text classification, enhanced search capabilities, and personalized recommendations, providing a comprehensive toolkit for data-driven decision-making. This versatility makes it an appealing choice for organizations aiming to optimize their machine learning processes while maintaining control over their data.
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
RINA Systems
Country
United States
Website
www.rinasystems.com/product/rinalogy-api.html
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization