Here's how SPARK.APACHE.ORG makes money* and how much!

*Please read our disclaimer before using our estimates.
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SPARK . APACHE . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Spark.apache.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. External Links
  10. Analytics And Tracking
  11. Libraries
  12. CDN Services

We are analyzing https://spark.apache.org/docs/latest/ml-guide.html.

Title:
MLlib: Main Guide - Spark 4.0.0 Documentation
Description:
No description found...
Website Age:
30 years and 2 months (reg. 1995-04-11).

Matching Content Categories {📚}

  • Automotive
  • Technology & Computing
  • Science

Content Management System {📝}

What CMS is spark.apache.org built with?

Custom-built

No common CMS systems were detected on Spark.apache.org, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of spark.apache.org audience?

🌟 Strong Traffic: 100k - 200k visitors per month


Based on our best estimate, this website will receive around 114,247 visitors per month in the current month.

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How Does Spark.apache.org Make Money? {💸}

We're unsure if the website is profiting.

Not all websites are made for profit; some exist to inform or educate users. Or any other reason why people make websites. And this might be the case. Spark.apache.org could have a money-making trick up its sleeve, but it's undetectable for now.

Keywords {🔍}

spark, api, mllib, added, rddbased, guide, pipelines, dataframebased, classification, feature, learning, linear, algebra, features, machine, algorithms, libraries, statistics, data, regression, clustering, collaborative, filtering, transformation, library, models, maintenance, mode, package, support, dataframes, scala, native, acceleration, python, classifier, basic, frequent, pattern, mining, model, selection, tuning, dimensionality, reduction, extraction, practical, high, tools, load,

Topics {✒️}

collaborative filtering featurization tree-based feature transformation mllib rdd-based api mllib dataframe-based api rdd-based api native acceleration libraries accelerated native libraries dataframes-based api dimensionality reduction dataframe-based api rdd-based apis reach feature parity factorization machines classifier selection pipelines user-friendly api machine learning library sql/dataframe queries optimised numerical processing1 gradient boosted trees watch sam halliday ml function parity feature extraction common learning algorithms pure jvm implementation system optimised natives runtime library paths sample weights support data handling selecting features classification pipelines guide linear algebra multiple columns support entered maintenance mode ml scala package machine learning migration guide primary api guide mllib regression clustering statistics pipelines utilities feature transformations dataframes provide system libraries uniform api ml package ml algorithms classification models high level

Questions {❓}

  • Is MLlib deprecated?
  • What are the implications?
  • What is “Spark ML”?
  • Why is MLlib switching to the DataFrame-based API?

External Links {🔗}(31)

Analytics and Tracking {📊}

  • Piwik/Matomo

Libraries {📚}

  • Bootstrap
  • jQuery
  • Modernizr

CDN Services {📦}

  • Cloudflare
  • Mathjax

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