Post on 21-Jan-2017
H2O.ai Machine Intelligence
Machine Learning for the
Sensored Internet of Things
Hank Roarkhank@h2o.ai@hankroark
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
H2O.ai Machine Intelligence
This much data will require a fast OODA loopMuch 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
H2O.ai Machine Intelligence
Machine Prognostics Use Case Sensor data of turbofan remaining useful life prediction
Jupyter notebook @ https://goo.gl/G2zx3o
Many more tips and tricks
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, ... , feature
engineering
• 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 bronobytes of data