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Average Ratings 0 Ratings
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
A hybrid front-end efficiently switches between Gluon eager imperative mode and symbolic mode, offering both adaptability and speed. The framework supports scalable distributed training and enhances performance optimization for both research and real-world applications through its dual parameter server and Horovod integration. It features deep compatibility with Python and extends support to languages such as Scala, Julia, Clojure, Java, C++, R, and Perl. A rich ecosystem of tools and libraries bolsters MXNet, facilitating a variety of use-cases, including computer vision, natural language processing, time series analysis, and much more. Apache MXNet is currently in the incubation phase at The Apache Software Foundation (ASF), backed by the Apache Incubator. This incubation stage is mandatory for all newly accepted projects until they receive further evaluation to ensure that their infrastructure, communication practices, and decision-making processes align with those of other successful ASF initiatives. By engaging with the MXNet scientific community, individuals can actively contribute, gain knowledge, and find solutions to their inquiries. This collaborative environment fosters innovation and growth, making it an exciting time to be involved with MXNet.
API Access
Has API
API Access
Has API
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon EC2 Inf1 Instances
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Apache Cassandra
Apache HBase
Apache Spark
Cameralyze
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon EC2 Inf1 Instances
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Apache Cassandra
Apache HBase
Apache Spark
Cameralyze
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
The Apache Software Foundation
Founded
1999
Country
United States
Website
mxnet.apache.org
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization