In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Practical Machine Learning Techniques for Building Intelligent Applications

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Practical Machine Learning Techniques for Building Intelligent Applications

O'Reilly Radar