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Automunge_twiml-brief.md

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Prepare Tabular Data for ML with Automunge

Presenter

Nicholas Teague

website: automunge.com

twitter: @_NicT_

Context

Mainstream machine learning libraries have several prerequisites:

  • numerical encoded data
  • normalized sets
  • all valid entries
  • consistent transforms between training and test data

Automunge is a platform for preparing tabular data for machine learning.

Description

Automunge is an open source python library. Through application tabular training data may be prepared to meet the prerequisites of machine learning, and subsequent data may easily be prepared on a consistent basis.

Automunge is suitable for both novice and expert alike, as transformations may be performed under automation, or users may also engineer custom data pipelines.

Some highlights include:

  • automated ML derived missing data infill aka "ML infill"
  • extensive library of data transformations, or users may define their own
  • simple specification of transformation sets that may include generations and branches of derivations
  • automated feature importance evaluation
  • automated dimensionality reductions
  • automated evaluation of data property drift between training data and subsequent data

Documentation

Full documentation is availalbe on GitHub

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