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.

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.

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.

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.

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.

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

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

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

From Airbnb, used in their automated pricing

Logistic regression, LDA, Factorization machines, Neural Network, Restricted Boltzmann Machines

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.

Export PMML, an industry standard XML format for transporting machine learning models.

Adds arbitrary distance functions to K-Means

Visualize the Streaming Machine Learning algorithms built into Spark MLlib

Factorization Machines

Recursive Neural Networks (RNNs)

SVM based on the performant Spark communication framework CoCoA listed above.

Matrix Factorization Recommendation System

40x faster clustering than Spark MLlib K-Means

Build graphs using k-nearest-neighbors and locality sensitive hashing (LSH)

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

Adaboost and MP-Boost

Linear algebra operators to work with Spark MLlib’s linalg package

Sparse feature vectors

K-Means, Regression, and Statistics

Author: xxxxxxxx1x2xxxxxxx
Title: Spark的39个机器学习库





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