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Brendan Herger [email protected]
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 / CRANStrengths
• Algorithm selection - CRAN Library
• Community
• Commercial backing - Revolution Analytics / RStudio
• Packages (Plotting - GGPlot 2, Time series - SARIMA / GARCH)
R / CRANWeaknesses
• Algorithm implementation
• Documentation
• Computational limits
• Ongoing support
R / CRANRoadmap
• Go write it
• Commercial support: Multi-threading, scalability, Hadoop map-reduce
Pandas / SKLearn
Pandas / SKLearnStrengths
• Unified APIs
• Algorithm Choice / quick iteration
• Documentation
• Other Python packages
Pandas / SKLearnWeaknesses
• Limited to one machine
• Two packages shimmed together
• Python broilerplate
Pandas / SKLearnRoadmap
• 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 MLLibStrengths
• Commercial Backing
• Programming Languages
• Community
Spark MLLibWeaknesses
• Algorithm Implementations
• Documentation
• Community
Spark MLLibRoadmap
Spark MLLibRoadmap
• ML - Pipelines for MLLib
• “Datasets”: RDDs + “Data Frames”
H2O.ai
H2O.aiStrengths
• Speed & Data Scale
• Programming Languages
• Commercial Backing
H2O.aiWeaknesses
• Algorithm Choice, Utility Functions
• Community Support
H2O.aiRoadmap
• Go to the Keynote talks
Case Study
Which is best?
[ ] is best
Thanks! [email protected]
