MLlib is Spark's machine learning library, focusing on learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.

We are launching a product which will configure and publish Spark MLlib, an open source software solution which is embedded pre-configured tool with Ubuntu OS and ready-to-launch VM on Azure that contains Spark MBlib, Hadoop 2.7, Scala, Linux, PHP (LAMP). MLlib fits into Spark's APIs and interoperates with Scala. You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.

Why MLlib? It is built on Apache Spark, which is a fast and general engine for large scale processing. Supposedly, running times or up to 100x faster than Hadoop MapReduce, or 10x faster on disk. Supports writing applications in Java, Scala, or Python.

MLlib contains many algorithms and utilities

Classification: logistic regression, naive Bayes

Regression: generalized linear regression, survival regression

Decision trees, random forests, and gradient-boosted trees

Recommendation: alternating least squares (ALS)

Clustering: K-means, Gaussian mixtures (GMMs)

Topic modeling: latent Dirichlet allocation (LDA)

Frequent itemsets, association rules, and sequential pattern mining

MLlib will still support the RDD-based API in spark.mllib with bug fixes.

MLlib will not add new features to the RDD-based API.

In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.

After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.

The RDD-based API is expected to be removed in Spark 3.0.

The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.

DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the Pipelines guide for details.

Data types

Classification and regression

Collaborative filtering

Clustering

Dimensionality reduction

Feature extraction and transformation