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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

Scala seamlessly integrates both object-oriented and functional programming paradigms into a single, elegant high-level language. With its static type system, Scala minimizes the likelihood of errors in intricate applications, while its compatibility with JVM and JavaScript allows developers to create efficient systems that can leverage extensive libraries. The Scala compiler is adept in managing static types, meaning that in most instances, you don't need to specify variable types; its robust type inference handles this automatically. Structural data types in Scala are represented by case classes, which automatically provide well-defined methods for toString, equals, and hashCode, in addition to enabling deconstruction through pattern matching. Moreover, in Scala, functions are treated as first-class citizens, allowing for the creation of anonymous functions using a streamlined syntax. This versatility makes Scala an appealing choice for developers seeking a language that combines the best of both programming worlds.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Ably No 
Amazon Q Business No 
Claude Opus 4 No 
Claude Sonnet 3.5 No 
DeepSeek-Coder-V2 No 
GPT-5.3-Codex No 
GPT‑5-Codex No 
Grok 4 No 
Grok 4.20 No 
Java Yes 
JupyterLab No 
Kiro No 
MLlib No 
MapReduce Yes 
Mistral AI No 
Mixtral 8x22B No 
OpenAI o1 No 
SonarQube Server No 
codebeat No 
{CodeStore} No 

Integrations

Ably Yes 
Amazon Q Business Yes 
Claude Opus 4 Yes 
Claude Sonnet 3.5 Yes 
DeepSeek-Coder-V2 Yes 
GPT-5.3-Codex Yes 
GPT‑5-Codex Yes 
Grok 4 Yes 
Grok 4.20 Yes 
Java No 
JupyterLab Yes 
Kiro Yes 
MLlib Yes 
MapReduce No 
Mistral AI Yes 
Mixtral 8x22B Yes 
OpenAI o1 Yes 
SonarQube Server Yes 
codebeat Yes 
{CodeStore} 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 Yes 
Mac Yes 
Linux Yes 
Chromebook Yes 

Customer Support

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

Customer Support

Business Hours No 
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 No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Vendor Details

Company Name

Scala

Website

www.scala-lang.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

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