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.

How can sound be used to both generate data and express data? In this episode of the Hardware podcast, we talk with Cameron Turner, co-founder and principal at The Data Guild. Turner is the author of the new O’Reilly report “Finding Profit in Your Organization’s Data: Examples and Best Practices.”

In this episode of the O’Reilly Data Show, I spoke with one of Strata + Hadoop World’s most popular teachers—Duncan Ross, data and analytics director at TES Global. In his long career in data, Ross has seen several stages of the evolution of tools, techniques, and training programs, and along the way he has interacted with business managers in many countries and regions. In keeping with his wide-ranging interests, we discussed many topics, including business analytics, data science training programs, data philanthropy and data for good, and university rankings.

In this episode of the O’Reilly Data Show, I spoke with M.C. Srivas, co-founder of MapR and currently chief architect for data at Uber. We discussed his long career in data management and his experience building a variety of distributed systems. In the course of his career, Srivas has architected key components that now comprise many data platforms (distributed file system, database, query engine, messaging system, etc.).

In this episode of the O'Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor.

We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain.

In this new episode of the Hardware Podcast, David Cranor and I talk with data scientist Rachel Kalmar, formerly with Misfit Wearables and the founder and organizer of the Sensored Meetup in San Francisco. She shares insights from her work at the intersection of data, hardware, and health care.

In this episode of the O’Reilly Data Show, I spoke with one of the most popular speakers at Strata+Hadoop World:  Joe Hellerstein, professor of Computer Science at UC Berkeley and co-founder/CSO of Trifacta. We talked about his past and current academic research (which spans HCI, databases, and systems), data wrangling, large-scale distributed systems, and his recent work on metadata services.

I spoke with Eric Colson, chief algorithms officer at Stitch Fix, and former VP of data science and engineering at Netflix. We talked about building and deploying mission-critical, human-in-the-loop systems for consumer Internet companies. Knowing that many companies are grappling with incorporating data science, I also asked Colson to share his experiences building, managing, and nurturing, large data science teams at both Netflix and Stitch Fix.

I sat down with Vasant Dhar, a professor at the Stern School of Business and Center for Data Science at NYU, founder of SCT Capital Management, and editor-in-chief of the Big Data Journal (full disclosure: I'm a member of the editorial board). We talked about the early days of AI and data mining, and recent applications of data science to financial investing and other domains.

In this special holiday episode of the O’Reilly Data Show, I look back at two conversations I had earlier this year at the Spark Summit in San Francisco. The first segment is an on-stage fireside chat with Ben Horowitz, co-founder of Andreessen Horowitz and author of The Hard Thing About Hard Things.