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   <b>MLlib</b> is Apache Spark's scalable machine learning library.
 </div>

 <div class="row row-padded">
   <div class="col-md-7 col-sm-7">
     <h2>Ease of use</h2>
     <p class="lead">
       Usable in Java, Scala, Python, and R.
     </p>
     <p>
       MLlib fits into <a href="/">Spark</a>'s
       APIs and interoperates with <a href="http://www.numpy.org">NumPy</a>
       in Python (as of Spark 0.9) and R libraries (as of Spark 1.5).
       You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it
       easy to plug into Hadoop workflows.
     </p>
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     <div style="margin-top: 15px; text-align: left; display: inline-block;">
       <div class="code">
         data = spark.read.format(<span class="string">"libsvm"</span>)\<br />
 	    &nbsp;&nbsp;.load(<span class="string">"hdfs://..."</span>)<br />
         <br />
         model = <span class="sparkop">KMeans</span>(k=10).fit(data)
       </div>
       <div class="caption">Calling MLlib in Python</div>
     </div>
   </div>
 </div>

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     <h2>Performance</h2>
     <p class="lead">
       High-quality algorithms, 100x faster than MapReduce.
     </p>
     <p>
       Spark excels at iterative computation, enabling MLlib to run fast.
       At the same time, we care about algorithmic performance:
       MLlib contains high-quality algorithms that leverage iteration, and
       can yield better results than the one-pass approximations sometimes used on MapReduce.
     </p>
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       <img src="/images/logistic-regression.png" style="width: 100%; max-width: 250px;" />
       <div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
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     <h2>Runs everywhere</h2>
     <p class="lead">
       Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, against diverse data sources.
     </p>
     <p>
       You can run Spark using its <a href="/docs/latest/spark-standalone.html">standalone cluster mode</a>,
       on <a href="https://github.com/amplab/spark-ec2">EC2</a>,
       on <a href="https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">Hadoop YARN</a>,
       on <a href="https://mesos.apache.org">Mesos</a>, or
       on <a href="https://kubernetes.io/">Kubernetes</a>.
       Access data in <a href="https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html">HDFS</a>,
       <a href="https://cassandra.apache.org">Apache Cassandra</a>,
       <a href="https://hbase.apache.org">Apache HBase</a>,
       <a href="https://hive.apache.org">Apache Hive</a>,
       and hundreds of other data sources.
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   <div class="col-md-4 col-padded">
     <h3>Algorithms</h3>
     <p>
       MLlib contains many algorithms and utilities.
     </p>
     <p>
       ML algorithms include:
     </p>
     <ul class="list-narrow">
       <li>Classification: logistic regression, naive Bayes,...</li>
       <li>Regression: generalized linear regression, survival regression,...</li>
       <li>Decision trees, random forests, and gradient-boosted trees</li>
       <li>Recommendation: alternating least squares (ALS)</li>
       <li>Clustering: K-means, Gaussian mixtures (GMMs),...</li>
       <li>Topic modeling: latent Dirichlet allocation (LDA)</li>
       <li>Frequent itemsets, association rules, and sequential pattern mining</li>
     </ul>
     <p>
       ML workflow utilities include:
     </p>
     <ul class="list-narrow">
       <li>Feature transformations: standardization, normalization, hashing,...</li>
       <li>ML Pipeline construction</li>
       <li>Model evaluation and hyper-parameter tuning</li>
       <li>ML persistence: saving and loading models and Pipelines</li>
     </ul>
     <p>
       Other utilities include:
     </p>
     <ul class="list-narrow">
       <li>Distributed linear algebra: SVD, PCA,...</li>
       <li>Statistics: summary statistics, hypothesis testing,...</li>
     </ul>
     <p>Refer to the <a href="/docs/latest/ml-guide.html">MLlib guide</a> for usage examples.</p>
   </div>

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     <h3>Community</h3>
     <p>
       MLlib is developed as part of the Apache Spark project. It thus gets
       tested and updated with each Spark release.
     </p>
     <p>
       If you have questions about the library, ask on the
       <a href="/community.html#mailing-lists">Spark mailing lists</a>.
     </p>
     <p>
       MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib,
       read <a href="/contributing.html">how to
       contribute to Spark</a> and send us a patch!
     </p>
   </div>

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     <h3>Getting started</h3>
     <p>
       To get started with MLlib:
     </p>
     <ul class="list-narrow">
       <li><a href="/downloads.html">Download Spark</a>. MLlib is included as a module.</li>
       <li>Read the <a href="/docs/latest/ml-guide.html">MLlib guide</a>, which includes
       various usage examples.</li>
       <li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
         if you'd like to run in distributed mode. You can also run locally on a multicore machine
         without any setup.
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             <span class="small">(Apr 18, 2024)</span></li>

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             <span class="small">(Feb 23, 2024)</span></li>

           <li><a href="/news/spark-3-3-4-released.html">Spark 3.3.4 released</a>
             <span class="small">(Dec 16, 2023)</span></li>

           <li><a href="/news/spark-3-4-2-released.html">Spark 3.4.2 released</a>
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	<b>MLlib</b> is Apache Spark's scalable machine learning library.
	</div>

	<div class="row row-padded">
	<div class="col-md-7 col-sm-7">
	<h2>Ease of use</h2>
	<p class="lead">
	Usable in Java, Scala, Python, and R.
	</p>
	<p>
	MLlib fits into <a href="/">Spark</a>'s
	APIs and interoperates with <a href="http://www.numpy.org">NumPy</a>
	in Python (as of Spark 0.9) and R libraries (as of Spark 1.5).
	You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it
	easy to plug into Hadoop workflows.
	</p>
	</div>
	<div class="col-md-5 col-sm-5 col-padded-top col-center">

	<div style="margin-top: 15px; text-align: left; display: inline-block;">
	<div class="code">
	data = spark.read.format(<span class="string">"libsvm"</span>)\<br />
	.load(<span class="string">"hdfs://..."</span>)<br />
	<br />
	model = <span class="sparkop">KMeans</span>(k=10).fit(data)
	</div>
	<div class="caption">Calling MLlib in Python</div>
	</div>
	</div>
	</div>

	<div class="row row-padded">
	<div class="col-md-7 col-sm-7">
	<h2>Performance</h2>
	<p class="lead">
	High-quality algorithms, 100x faster than MapReduce.
	</p>
	<p>
	Spark excels at iterative computation, enabling MLlib to run fast.
	At the same time, we care about algorithmic performance:
	MLlib contains high-quality algorithms that leverage iteration, and
	can yield better results than the one-pass approximations sometimes used on MapReduce.
	</p>
	</div>
	<div class="col-md-5 col-sm-5 col-padded-top col-center">
	<div style="width: 100%; max-width: 272px; display: inline-block; text-align: center;">
	<img src="/images/logistic-regression.png" style="width: 100%; max-width: 250px;" />
	<div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
	</div>
	</div>
	</div>

	<div class="row row-padded" style="margin-bottom: 15px;">
	<div class="col-md-7 col-sm-7">
	<h2>Runs everywhere</h2>
	<p class="lead">
	Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, against diverse data sources.
	</p>
	<p>
	You can run Spark using its <a href="/docs/latest/spark-standalone.html">standalone cluster mode</a>,
	on <a href="https://github.com/amplab/spark-ec2">EC2</a>,
	on <a href="https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html">Hadoop YARN</a>,
	on <a href="https://mesos.apache.org">Mesos</a>, or
	on <a href="https://kubernetes.io/">Kubernetes</a>.
	Access data in <a href="https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html">HDFS</a>,
	<a href="https://cassandra.apache.org">Apache Cassandra</a>,
	<a href="https://hbase.apache.org">Apache HBase</a>,
	<a href="https://hive.apache.org">Apache Hive</a>,
	and hundreds of other data sources.
	</p>
	</div>
	<div class="col-md-5 col-sm-5 col-padded-top col-center">
	<img src="/images/hadoop.jpg" style="width: 100%; max-width: 280px;" />
	</div>
	</div>

	<div class="row">
	<div class="col-md-4 col-padded">
	<h3>Algorithms</h3>
	<p>
	MLlib contains many algorithms and utilities.
	</p>
	<p>
	ML algorithms include:
	</p>
	<ul class="list-narrow">
	<li>Classification: logistic regression, naive Bayes,...</li>
	<li>Regression: generalized linear regression, survival regression,...</li>
	<li>Decision trees, random forests, and gradient-boosted trees</li>
	<li>Recommendation: alternating least squares (ALS)</li>
	<li>Clustering: K-means, Gaussian mixtures (GMMs),...</li>
	<li>Topic modeling: latent Dirichlet allocation (LDA)</li>
	<li>Frequent itemsets, association rules, and sequential pattern mining</li>
	</ul>
	<p>
	ML workflow utilities include:
	</p>
	<ul class="list-narrow">
	<li>Feature transformations: standardization, normalization, hashing,...</li>
	<li>ML Pipeline construction</li>
	<li>Model evaluation and hyper-parameter tuning</li>
	<li>ML persistence: saving and loading models and Pipelines</li>
	</ul>
	<p>
	Other utilities include:
	</p>
	<ul class="list-narrow">
	<li>Distributed linear algebra: SVD, PCA,...</li>
	<li>Statistics: summary statistics, hypothesis testing,...</li>
	</ul>
	<p>Refer to the <a href="/docs/latest/ml-guide.html">MLlib guide</a> for usage examples.</p>
	</div>

	<div class="col-md-4 col-padded">
	<h3>Community</h3>
	<p>
	MLlib is developed as part of the Apache Spark project. It thus gets
	tested and updated with each Spark release.
	</p>
	<p>
	If you have questions about the library, ask on the
	<a href="/community.html#mailing-lists">Spark mailing lists</a>.
	</p>
	<p>
	MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib,
	read <a href="/contributing.html">how to
	contribute to Spark</a> and send us a patch!
	</p>
	</div>

	<div class="col-md-4 col-padded">
	<h3>Getting started</h3>
	<p>
	To get started with MLlib:
	</p>
	<ul class="list-narrow">
	<li><a href="/downloads.html">Download Spark</a>. MLlib is included as a module.</li>
	<li>Read the <a href="/docs/latest/ml-guide.html">MLlib guide</a>, which includes
	various usage examples.</li>
	<li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
	if you'd like to run in distributed mode. You can also run locally on a multicore machine
	without any setup.
	</li>
	</ul>
	</div>
	</div>

	<div class="row">
	<div class="col-sm-12 col-center">
	<a href="/downloads.html" class="btn btn-cta btn-lg btn-multiline">
	Download Apache Spark<br /><span class="small">Includes MLlib</span>
	</a>
	</div>
	</div>

	</div>
	<div class="col-12 col-md-3">
	<div class="news" style="margin-bottom: 20px;">
	<h5>Latest News</h5>
	<ul class="list-unstyled">

	<li><a href="/news/spark-3-4-3-released.html">Spark 3.4.3 released</a>
	<span class="small">(Apr 18, 2024)</span></li>

	<li><a href="/news/spark-3-5-1-released.html">Spark 3.5.1 released</a>
	<span class="small">(Feb 23, 2024)</span></li>

	<li><a href="/news/spark-3-3-4-released.html">Spark 3.3.4 released</a>
	<span class="small">(Dec 16, 2023)</span></li>

	<li><a href="/news/spark-3-4-2-released.html">Spark 3.4.2 released</a>
	<span class="small">(Nov 30, 2023)</span></li>

	</ul>
	<p class="small" style="text-align: right;"><a href="/news/index.html">Archive</a></p>
	</div>
	<div style="text-align:center; margin-bottom: 20px;">
	<a href="https://www.apache.org/events/current-event.html">
	<img src="https://www.apache.org/events/current-event-234x60.png" style="max-width: 100%;"/>
	</a>
	</div>
	<div class="hidden-xs hidden-sm">
	<a href="/downloads.html" class="btn btn-cta btn-lg d-grid" style="margin-bottom: 30px;">
	Download Spark
	</a>
	<p style="font-size: 16px; font-weight: 500; color: #555;">
	Built-in Libraries:
	</p>
	<ul class="list-none">
	<li><a href="/sql/">SQL and DataFrames</a></li>
	<li><a href="/streaming/">Spark Streaming</a></li>
	<li><a href="/mllib/">MLlib (machine learning)</a></li>
	<li><a href="/graphx/">GraphX (graph)</a></li>
	</ul>
	<a href="/third-party-projects.html">Third-Party Projects</a>
	</div>
	</div>
	</div>



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