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| <title>Machine Learning - Apache Ignite</title> |
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| <section id="machine-learning" class="page-section"> |
| <h1 class="first">Machine Learning<sup><span style="font-size: 20px;">βeta</span></sup></h1> |
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| <div class="col-sm-6 col-md-7 col-xs-12" style="padding-left:0; padding-right:0;"> |
| <p>Apache Ignite 2.0 release introduced first version of its own distributed Machine Learning (ML) library called ML Grid.</p> |
| <p> |
| The rationale for building ML Grid is quite simple. Many users employ Ignite as the central high-performance storage and processing |
| systems for various data sets. If they wanted to perform ML or Deep Learning (DL) on these data sets (i.e training sets or model inference) |
| they had to ETL them first into some other systems like Apache Mahout or Apache Spark. |
| </p> |
| <p> |
| Presently ML Grid supports core distributed algebra implementation based on Ignite co-located distributed processing |
| as well as essential machine learning algorithms such as Linear Regression, Decision Trees, K-Means clustering and more. |
| Future releases will introduce custom DSLs for Python, R and Scala, |
| growing collection of optimized ML algorithms as well support for Ignite-optimized Neural Networks. |
| </p> |
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| <img class="img-responsive" src="/images/machine_learning.png" width="440px" style="float:right;"/> |
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| <p><a href="https://apacheignite.readme.io/docs/machine-learning" target="_blank">Read more</a></p> |
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