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The ability to instrument and interrogate data as it moves through a processing pipeline is fundamental to effective machine learning at scale. Applied in this capacity, information visualization technologies drive product innovation, shorten iteration cycles, reduce uncertainty, and ultimately improve the performance of predictive models. It can be challenging, however, to understand where in a workflow to employ data visualization, and, once committed to doing so, developing revealing visualizations that suggest clear next steps can be similarly daunting. In this talk we’ll describe the role that information visualization technologies play in the LinkedIn data science ecosystem, and explore best practices for understanding the structure of large-scale data in a production environment. From hypothesis generation and feature development to model evaluation and tooling, visualization is at the heart of LinkedIn’s machine learning workflows, enabling our data scientists to reason and communicate more effectively. Broken down into clear, structured insights based on proven technology and workflow patterns, this talk will help you understand how to apply information visualization to the analytical challenges you encounter every day. Meet Michael Conover Mike Conover builds machine learning technologies that leverage the behavior and relationships of hundreds of millions of people. A senior data scientist at LinkedIn, Mike has a Ph.D. in complex systems analysis with a focus on information propagation in large-scale social networks. His work has appeared in the New York Times, the Wall Street Journal, Science, MIT Technology Review and on National Public Radio.
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Information Visualization for Large-Scale Data Workflows
Michael ConoverSenior Data Scientist, LinkedIn
@vagabondjack
Emergent Structure
Credit Pedro Cruz, University of CoimbraDavid Crandall, Indiana UniversityJohn Nelson, IDV Solutions Elegant Complexity
Intellectual Dividends
Realistic Mental Models
Verification of Assumptions
Shortened Iteration Cycles
Improved Predictive Performance
Product Insights
Clarity of Communication
Hypothesis Generation
@whitehouse #RSVP
Color Commentary
Flock Together
Political Polarization on Twitter
Feature Development
Anscombe’s Quartet
http://en.wikpedia.org/Anscombe’s_quartet
Basic Workflow Structure
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geom_point()
A Lens on the Joint Distribution
geom_point(alpha=1/5)
A Lens on the Joint Distribution
geom_bin2d(bins=35)
A Lens on the Joint Distribution
geom_point(alpha=1/5, aes(color=label))
A Lens on the Joint Distribution
geom_density2d(aes(color=label), bins=20)
A Lens on the Joint Distribution
Marginal Histograms
A Lens on the Joint Distribution
GGally (ggpairs)
A Lens on the Joint Distribution
Model Fitting & Evaluation
Model A Model B
Training Data I
Training Data II
Evaluation Batteries
stanford.edu/~jhuang11/ Homework at Scale
github.com/StanfordHCI/termiteTopic Modeling
Layercake
Workflow Principles
Latent, Pervasive
Modular
Consistent Visual Language
Workflow Management
data.linkedin.com/opensource/azkabanLinkedIn Azkaban
data.linkedin.com/opensource/white-elephant LinkedIn White Elephant
github.com/Netflix/Lipstick Netflix Lipstick
driven.cascading.io Concurrent Driven
bigml.com BigML Sunburst
Information Visualization for Large-Scale Data Workflows
Michael ConoverSenior Data Scientist, LinkedIn
@vagabondjack
Tableau
RStudio Shiny
rstudio.com/shiny/
RStudio Shiny
rweb.stat.ucla.edu/ggplot2/
Data Driven Documents (D3)
d3js.org
Superconductor
superconductor.github.io
Stamen Tiles
maps.stamen.com
