39 Machine Learning Libraries for Spark, Categorized

Apache Spark itself

1. MLlib

AMPLab

Spark originally came out of Berkeley AMPLab and even today AMPLab projects, even though they are not in Apache Spark Foundation, enjoy a status a bit over your everyday github project.

ML Base

Spark's own MLLib forms the bottom layer of the three-layer ML Base, with MLI being the middle layer and ML Optimizer being the most abstract layer.

2. MLI

3. ML Optimizer (aka Ghostface)
Ghostware was described in 2014 but never released. Of the 39 machine learning libraries, this is the only one that is vaporware, and is included only due to its AMPLab and ML Base status.

Other than ML Base

4. Splash
A recent project from June, 2015, this set of stochastic learning algorithms claims 25x - 75x faster performance than Spark MLlib on Stochastic Gradient Descent (SGD). Plus it's an AMPLab project that begins with the letters "sp", so it's worth watching.

5. Keystone ML
Brought machine learning pipelines to Spark, but pipelines have matured in recent versions of Spark. Also promises some computer vision capability, but there are limitations I previously blogged about.

6. Velox
A server to manage a large collection of machine learning models.

7. CoCoA
Faster machine learning on Spark by optimizing communication patterns and shuffles, as described in the paper Communication-Efficient Distributed Dual Coordinate Ascent

Frameworks

GPU-based

8. DeepLearning4j
I previously blogged DeepLearning4j Adds Spark GPU Support

9. Elephas
Brand new and frankly why I started this list for this blog post. Provides an interface to Keras.

Non-GPU-based

10. DistML
Parameter server for model-parallel rather than data-parallel (as Spark's MLlib is).

11. Aerosolve
From Airbnb, used in their automated pricing

12. Zen
Logistic regression, LDA, Factorization machines, Neural Network, Restricted Boltzmann Machines

13. Distributed Data Frame
Similar to Spark DataFrames, but agnostic to engine (i.e. will run on engines other than Spark in the future). Includes cross-validation and interfaces to external machine learning libraries.

Interfaces to other Machine Learning systems

14. spark-corenlp
Wraps Stanford CoreNLP.

15. Sparkit-learn
Interface to Python's Scikit-learn

16. Sparkling Water
Interface to H₂O

17. hivemall-spark
Wraps Hivemall, machine learning in Hive

18. spark-pmml-exporter-validator
Export PMML, an industry standard XML format for transporting machine learning models.

Add-ons that enhance MLlib's existing algorithms

19. MLlib-dropout
Adds dropout capability to Spark MLLib, based on the paper Dropout: A simple way to prevent neural networks from overfitting.

20. generalized-kmeans-clustering
Adds arbitrary distance functions to K-Means

21. spark-ml-streaming
Visualize the Streaming Machine Learning algorithms built into Spark MLlib

Algorithms

Supervised learning

22. spark-libFM
Factorization Machines

23. ScalaNetwork
Recursive Neural Networks (RNNs)

24. dissolve-struct
SVM based on the performant Spark communication framework CoCoA listed above.

25. Sparkling Ferns
Based on Image Classification using Random Forests and Ferns

26. streaming-matrix-factorization
Matrix Factorization Recommendation System

Unsupervised learning

27. PatchWork
40x faster clustering than Spark MLlib K-Means

28. Bisecting K-Meams Clustering
K-Means that produces more uniformly-sized clusters, based on A Comparison of Document Clustering Techniques

29. spark-knn-graphs
Build graphs using k-nearest-neighbors and locality sensitive hashing (LSH)

30. TopicModeling
Online Latent Dirichlet Allocation (LDA), Gibbs Sampling LDA, Online Hierarchical Dirichlet Process (HDP)

Algorithm building blocks

31. sparkboost
Adaboost and MP-Boost

32. spark-tfocs
Port to Spark of TFOCS: Templates for First-Order Conic Solvers. If your machine learning cost function happens to be convex, then TFOCS can solve it.

33. lazy-linalg
Linear algebra operators to work with Spark MLlib's linalg package

Feature extractors

34. spark-infotheoretic-feature-selection
Information-theoretic basis for feature selection, based on Conditional likelihood maximisation: a unifying framework for information theoretic feature selection

35. spark-MDLP-discretization
Given labeled data, "discretize" one of the continuous numeric dimensions such that each bin is relatively homogenous in terms of data classes. This is a foundational idea CART and ID3 algorithms to generate decision trees. Based on Multi-interval discretization of continuous-valued attributes for classification learning.

36. spark-tsne
Distributed t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction.

37. modelmatrix
Sparse feature vectors

Domain-specific

38. Spatial and time-series data
K-Means, Regression, and Statistics

39. Twitter data

UPDATE 2015-09-30: Although it was a reddit.com post regarding the Spark deep learning framework Elephas that kicked me off compiling this list, most of the rest comes from AMPLab and spark-packages.org, plus a couple came from memory. Check AMPLab and spark-packages.org for future updates (since this blog post is a static list). And for tips on how to keep up in general on the fast-moving Spark Ecosystem, see my 10-minute presentation from February, 2015 (scroll down to the second presentation of that mini-conference).