In February's Stride Tech Talk, CEO Debbie Madden discusses ed tech, machine learning, and the evolving landscape of education with Nick Orton, Manager, Platform Engineering at Knewton.
Knewton is at the forefront of ed tech. Classrooms are making a historical shift from print to digital learning materials. Why now?
The entire world is making a historic transition from the concrete to the digital. Education is no exception. This shift not only enables greater access to educational content — but also enables personalization of that content. With our new capacity to capture data and analyse it, we can deliver personalized, world-class educational materials to students on a global scale.
How can developers/CTOs/tech folks keep their skills sharp so they are attractive potential employees for ed tech companies like Knewton?
Technology is a constantly changing landscape. the threat of becoming unemployable because of outdated skills is very terrifying and very real. A love of learning is just as an important engineering trait as attention to detail. Fortunately we live in a world where the channels available for education are growing exponentially. From Coursera to Code Academy, school is as far away as your computer. The problem then shifts to one of selecting what skills to pursue. For this one needs to stay actively engaged in their professional community to maintain an awareness of problems people are dealing with and the techniques they are using.
Machine learning is in the spot light these days, why do you think that is?
Sometimes I wonder if people would be as excited if it was called "advanced statistical modeling." But I think the main reason for interest is that every day people are directly engaging with amazing products created using these techniques. From the handwriting recognition software that I'm using to write this, to automated art criticism, ML is making it finally feel like the future around here. Pretty much every college kid that I talk to is taking a machine learning class. When I was in school, it wasn't even on my radar. I expect more great things to come.
What would you suggest for tech folks that want to break into machine learning?
Coursera has a very popular course they offer regularly. There are also a ton of open source projects and data sets with which people can engage. Machine Learning technologies are a lot more then data science. I think people get stuck on the modelling, but without a lot of nuts and bolts engineering, those models aren't going to do anything interesting. ML involvement is a continuum with Data Analysts sitting on one side and Product / UX folks sitting at the other. Personally I've found a nice comfortable niche in the middle with Platform engineering.
You've grown up in NYC tech over the last 10 years. What do you know now that you wish you knew when you started out?
Um ... everything? But seriously, I wish I started out understanding the landscape of roles that are available for technologists. I started out thinking that there was one job: "programmer". Now I have an appreciation for the skill sets that set a good test engineer apart from, say, a good site reliability engineer. Perhaps if I understood the market better, I could have navigated my career more deliberately. If you're a recruiter reading this, spend some time to educate the kids you pick up straight out of college. They'll remember you for it.