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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

Discover the transformative capabilities of large language models as they redefine Natural Language Processing (NLP) through Spark NLP, an open-source library that empowers users with scalable LLMs. The complete codebase is accessible under the Apache 2.0 license, featuring pre-trained models and comprehensive pipelines. As the sole NLP library designed specifically for Apache Spark, it stands out as the most widely adopted solution in enterprise settings. Spark ML encompasses a variety of machine learning applications that leverage two primary components: estimators and transformers. Estimators possess a method that ensures data is secured and trained for specific applications, while transformers typically result from the fitting process, enabling modifications to the target dataset. These essential components are intricately integrated within Spark NLP, facilitating seamless functionality. Pipelines serve as a powerful mechanism that unites multiple estimators and transformers into a cohesive workflow, enabling a series of interconnected transformations throughout the machine-learning process. This integration not only enhances the efficiency of NLP tasks but also simplifies the overall development experience.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
Java Yes 
Python Yes 
R Yes 
Scala Yes 
ALBERT No 
APIFuzzer No 
Amazon EC2 Yes 
Apache Mesos Yes 
Databricks No 
ELMO No 
Facebook No 
Flair No 
Hadoop Yes 
Kubernetes Yes 
MapReduce Yes 
Maven No 
OpenAI Whisper No 
RoBERTa No 
TensorFlow No 

Integrations

Apache Spark Yes 
Java Yes 
Python Yes 
R Yes 
Scala Yes 
ALBERT Yes 
APIFuzzer Yes 
Amazon EC2 No 
Apache Mesos No 
Databricks Yes 
ELMO Yes 
Facebook Yes 
Flair Yes 
Hadoop No 
Kubernetes No 
MapReduce No 
Maven Yes 
OpenAI Whisper Yes 
RoBERTa Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Vendor Details

Company Name

John Snow Labs

Country

United States

Website

sparknlp.org

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

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