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H2O World - Survey of Available Machine Learning Frameworks - Brendan Herger

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Sri Ambati
Sri Ambati

H2O World 2015 - Brendan Herger of Capital One - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

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Machine Learning Frameworks Brendan Herger Brendan.Herger@CapitalOne.com
Brendan Herger Brendan.Herger@CapitalOne.com
https://xkcd.com/530/
Load Data Feature Engineering ML
Why are we here? There are many awesome, competing, overlapping machine learning libraries And people usually stick to one
Criteria Overview of libraries Case study
Criteria
Criteria • What each project does • Scalability • Usability • Integration
Overview of Libraries
R / CRAN
R / CRAN Strengths • Algorithm selection - CRAN Library • Community • Commercial backing - Revolution Analytics / RStudio • Packages (Plotting - GGPlot 2, Time series - SARIMA / GARCH)
R / CRAN Weaknesses • Algorithm implementation • Documentation • Computational limits • Ongoing support
R / CRAN Roadmap • Go write it • Commercial support: Multi-threading, scalability, Hadoop map-reduce
Pandas / SKLearn
Pandas / SKLearn Strengths • Unified APIs • Algorithm Choice / quick iteration • Documentation • Other Python packages
Pandas / SKLearn Weaknesses • Limited to one machine • Two packages shimmed together • Python broilerplate
Pandas / SKLearn Roadmap • Python: Multithreading (Project Ibis) • Pandas: Rapid development, better time series, better SKLearn integration • SKLearn: C Implementations of some algorithms, awaiting new algorithms
Spark MLLib
Spark MLLib Strengths • Commercial Backing • Programming Languages • Community
Spark MLLib Weaknesses • Algorithm Implementations • Documentation • Community
Spark MLLib Roadmap
Spark MLLib Roadmap • ML - Pipelines for MLLib • “Datasets”: RDDs + “Data Frames”
H2O.ai
H2O.ai Strengths • Speed & Data Scale • Programming Languages • Commercial Backing
H2O.ai Weaknesses • Algorithm Choice, Utility Functions • Community Support
H2O.ai Roadmap • Go to the Keynote talks
Case Study
Which is best? [ ] is best
Thanks! Brendan.Herger@CapitalOne.com

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