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There are many methods for doing automated Machine Learning. Early methods modeled biological or statistical building blocks in low level procedural code. The programming methodologies have evolved over the years to have libraries of the building blocks built and packaged for convenience. Even at a higher level, whole platforms have been packaged and provided in such a way that almost drag and drop methods can be used, with parameter choice made at different levels of automation.

Languages

Python

R

SQL

SAS

others (C/C++, java, etc.)

Libraries

Tensorflow (Google)

Keras

CoreML (Apple)

https://machinelearning.apple.com/

https://developer.apple.com/machine-learning

OpenCV

Pandas

Scikit-learn

MxNet

Big Data Platforms

CUDA (Program on NVIDIA GPUs)

Even Easier Introduction CUDA

GPU Accelerated Computing with Python

Hadoop (Cluster management using MapReduce algorithm)

Spark/Skala (New cluster management)

Cloud Services

(most available on-prem as well)

Google's AutoML

DataScience.com

Oracle Buys DataScience.com

DataRobot

Ersatz

Alteryx

C3iot.ai

H2O.ai

IBM Watson

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