What can machine learning do for us? How can we go further with our data, faster? How can we use the data that we generate today to move towards more intelligent clever products in a faster way? Senses have made a migration to a digital platform. We want to enable the digital smell in the Internet of Things applications. We’re in a different regime of the physics. We’re dealing with things on an impossibly small scale. We build chemical sensors. Because our sensors are small, we work with arrays of them, many many small sensors. That’s where the magic happens. All those sensors inform us about the chemistry of your environment. Quality of air, the safety of the air. We’re not well informed about the environments that we live, work, and play in. And we’re really answering that need. We have to understand more than just Reno Nevada. The world, at large, is much more complex. We test and calibrate every sensor that we send out the door. We test our sensors in all the environments that they could see and that’s what DataRobot helps with. We were building our algorithms by hand. That was a very manual process, labor intensive process. What DataRobot has allowed us to do is take a lot of that guesswork out of it and automate new routine things and not have to worry about mundane details and think about the bigger picture. I’m not a data scientist but the interface enables access to the important pieces, the salient features to quickly understand. As we move into new areas beyond flame walls and indoor air quality, DataRobot is what’s going to allow our engineers to work quickly and effectively on new problems. We’re using machine learning and A.I., coupled with really excellent chemical sensors, to really make the world a place to give people a better environment to live and live healthier, better lives.

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