Expert Interview (Part 1): Mike Olson on Cloudera, Machine Learning, and the Adoption of the Cloud
At the Cloudera Sessions event in Munich, Germany, Paige Roberts of Syncsort sat down with Mike Olson, Chief Strategy Officer of Cloudera. In this first of a three-part interview, Mike Olson goes into what’s new at Cloudera, how machine learning is evolving, and the adoption of the Cloud in organizations.
Paige: So, first off, go ahead and introduce yourself.
Mike: I’m Mike Olson from Cloudera. I’m a co-founder and am chief strategy officer of the company and I’m excited to speak with you, Paige.
I’m excited to speak with you too. So, what is going on at Cloudera right now that’s really exciting?
Well, I spent some time in the sessions here in Munich today talking about what we’re seeing in the adoption of machine learning and some of these advanced analytic techniques. That’s really exciting, like the use cases that are getting built, using these new analytic techniques…it’s pretty awesome. I mean in healthcare, diagnosing disease better than ever before, delivering better treatments. Even intervening in real time when patients need special care and we can detect that because they’re Internet connected, they’re wearing connected devices. So, lots of cool use cases.
That is pretty cool.
That’s driven by some investments that we’ve made over the last couple years. So, we bought a company in San Francisco called Sense.IO. That technology and that team basically turned into the Cloudera Data Science Workbench. I was really excited by that
I just saw a presentation earlier by somebody who said they were using it.
We think it makes developing those apps that much easier. About a month ago we concluded our acquisition of a really interesting Brooklyn-based machine learning research firm, Fast Forward Labs.
Hillary Mason! Yeah.
She’s been awesome for a long time, and now she’s running the research function at Cloudera, continuing to track the sort of cutting edge, what’s going to happen in ML (machine learning) and AI (artificial intelligence), applied to real enterprise workloads. So, we know more, we’ve got a much better informed opinion about that stuff now. I’m really excited about what that means for us.
That’s cool! So, is there anything that out on the landscape that you see coming that’s got you worried, or got you excited, or got you wondering?
If I were to highlight just a couple things, I wouldn’t say worried but respectfully attentive, large enterprises were definitely afraid of the Cloud before.
Yes, they were.
And now they’re clearly beginning to embrace the cloud. They’re trying to decide how to integrate their business practices into these new security regimes that the Cloud provides, and I absolutely believe the Cloud is at least as secure as your own data center, but you need to be sure that you’re using it properly, right?
But the shift from a traditional on-premises IT mindset to a cloudy one is confusing and disruptive to a lot of large enterprises, and we’re spending time with our clients, helping them think about …
Getting over the hump.
Yeah, that’s right. And I don’t know that I would say I’m worried about it. I think it’s a big opportunity. People can do stuff in the Cloud because it’s easy to spin up a bunch of infrastructure and do some work and then spin it back down and, you know you can never do that on-premises, right?
No, you can’t. You can’t commit to having a thousand-node cluster that you only need for two days. [Laughs]
No, that’s right. Who’s going to call their hardware vendor and say, I need three racks for a week, right? But you can do that on Amazon, on Azure, or on Google. So, helping them over that stumbling block is taking some time from us.
Yeah, I can understand that.
I told you all these reasons that I’m really excited about machine learning. If I were to highlight a modest concern I’ve got, it’s that ML is pretty hype-y, and maybe we’re contributing to that a little bit. We’re very bullish about it. I will say, we’ve got hundreds of customers actually doing it in production. This is real stuff. But you hear these terms like artificial intelligence and cognitive computing… and honestly, what we’re doing is training models on large amounts of historical data to recognize anomalous behavior and new data, it’s way more pragmatic and practical than words like cognitive computing make it sound. So, I worry that we’ll, as an industry, overpromise and then disappoint. These computers aren’t thinking, right?
Yeah, people are thinking SkyNet, and they’re getting Siri or Alexa. [Chuckle]
Exactly. By the way, Siri and Alexa are totally awesome in what they do. But if you really have it down, speaker independent voice recognition and then some good integrated search technology. That really isn’t the matrix.
[Laughs] No. We’re a little ways from bots taking over the world.
Make sure to check out part 2 of this interview, where Mike Olson goes into the Gartner hype cycle and what’s on the horizon.
Also, learn about 5 key Big Data trends in the coming year by checking out our report, 2018 Big Data Trends: Liberate, Integrate & Trust