2. H2O.ai
Machine Intelligence
Who am I?
▪ Data Scientist & Hacker @ H2O.ai
▪ Lecturer in Systems Thinking, University of Illinois at Urbana-Champaign
▪ John Deere, Research, Software Product Development, High Tech Ventures
▪ Lots of time dealing with data off of machines, equipment, satellites, radar,
hand sampled, and on.
▪ Geospatial and temporal / time series data almost all from sensors.
▪ Previously at startups and consulting (Red Sky Interactive, Nuforia,
NetExplorer, Perot Systems, a few of my own)
▪ Systems Design & Management MIT
▪ Physics Georgia Tech
6. H2O.ai
Machine Intelligence
This much data will require a fast OODA loop
Much of these models will then be used in control systems
Image courtesy http://www.telecom-cloud.net/wp-content/uploads/2015/05/Screen-Shot-2015-05-27-at-3.51.47-PM.png
8. H2O.ai
Machine Intelligence
Key take aways for modeling the sensored IoT
• Some sort of signal processing is usually helpful, but can introduce bias
• Smoothers, filters, frequency domain, interpolation, LOWESS, ... ,
aka feature engineering or post-processing
• Knowing a little about the physics of the system will be helpful here
• Validation strategy is important
• Easy to memorize due to autocorrelation
• Sometimes the simplest things work
• Treat each observation independently; Use time, location, as data elements
• Uncertainty is the name of the game
• Methods that will report out probabilities are often required (not shown here)
• The data can be big, get ready, it'll be a great ride
• Scalable tools like H2O will help you model the coming brontobytes of data