The O'Reilly Media Podcast 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. The O'Reilly Media Podcast features interviews with the people working on the forefront of technology.

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With the rise of mobile phones equipped with GPS, we're meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques. A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

For more on our interview, visit O'Reilly Radar: http://oreil.ly/1t7EcOQ

The science of moving dots

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

Johannes Thönes talks to Barry O’Reilly, Founder and CEO at ExecCamp, about his book Lean Enterprise: How High Performance Organizations Innovate At Scale. 

A lean enterprise is a large organization that manages to keep innovating while keeping its existing products in the market. O’Reilly talks about the idea of scientific experiments and the build-measure-learn loop popularized by the lean-startup method. 

He shares his experiment of an online wine seller using Twitter. He further discusses the challenges for enterprises trying to do something similar and introduce the three-horizon model, to manage innovative, growing, and new products. As an example of a successful lean enterprise, O’Reilly talks about GOV.UK, the British government’s new website.

Software Engineering Radio: Lean Enterprise Interview with Barry O'Reilly

Intercom content marketing manager Adam Risman talks with Riley Newman about the importance of early data investment for startups, making data accessible at scale, and why data is "the voice of the customer."

Riley Newman, Head Of Data Science At Airbnb

What does it take to build a data-driven culture? In this O’Reilly Podcast episode, I pose that question to Ashish Thusoo, founder of Qubole. With his co-founder Joydeep Sen Sarma, Thusoo built a self-service data infrastructure at Facebook beginning in 2007. That infrastructure transformed Facebook’s culture and became part of practically every product decision that the company made.

Building a Data-Driven Culture

In this podcast episode, I talk multi-model databases with Damon Feldman, solutions director at MarkLogic. If this conversation gets you interested in multi-model databases, be sure to check out his live webcast on December 1, 2016.

Damon Feldman on Multi-Model Databases

In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.

Deep Learning That's Easy to Implement and Easy to Scale

In this episode of the O’Reilly podcast, Jon Bruner sat down with Rahul Kamdar, director of product management and strategy at TIBCO Software. They discussed the shift from the centralized enterprise service bus (ESB) to a distributed data architecture based on microservices, APIs, and cloud-native applications.

From Enterprise Service Bus to Microservices

This week, I sit down with Matt LeMay, product coach and consultant, and author of "Product Management in Practice." We talk about the four guiding principles of product management, what he has learned about himself as a product manager, and how to conduct meaningful research.

Matt LeMay on the Four Principles of Product Management

In this episode of the O’Reilly Programming Podcast, I talk serverless architecture with Mike Roberts, engineering leader and co-founder of Symphonia, a serverless and cloud architecture consultancy. Roberts will give two presentations—Serverless Architectures: What, Why, Why Not, and Where Next? and Designing Serverless AWS Applications—at the O’Reilly Software Architecture Conference, October 16-19, 2017, in London.

Mike Roberts on Serverless Architectures

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

In a recent episode of the O’Reilly Media Podcast, we spoke with George Miranda about the importance of service mesh technology in creating reliable distributed systems. As discussed in the new report The Service Mesh: Resilient Service-to-Service Communication for Cloud Applications, service mesh technology has emerged as a popular tool for companies looking to build cloud-native applications that are reliable and secure.

During the podcast, we discussed the problems a service mesh infrastructure solves and the service mesh features you’ll find most valuable. We also talked about how to choose the right service mesh for your organization, the challenges involved in getting it deployed to production, and the best ways for getting started with a service mesh.

How to Choose and Deploy a Service Mesh

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data scientists work, in hopes of inspiring some transfer learning from software development to data science.

Relevant links:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 1: The Good

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics!

Relevant links:
https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 2: Where Modifications Help

In this episode of the O’Reilly Media Podcast, Rachel Roumeliotis, VP of content strategy at O’Reilly, sat down with Daniel Krook, IBM developer advocate. They discussed how developers across industries can participate in the Call for Code initiative, the benefits of the program, support from its charitable partners—United Nations Human Rights and the American Red Cross—and the positive impacts IBM hopes to achieve by investing in Call for Code.

Developers Worldwide Will Collaborate on Solutions to Save Lives in Call For Code

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

The Three Types of Data Scientists, and What They Actually Do

This episode of the O’Reilly Podcast, features a conversation on serverless and Kubernetes, with Kelsey Hightower, developer advocate for Google Cloud Platform at Google (and co-author of "Kubernetes: Up and Running"), and Chris Gaun, Kubernetes product marketing manager at Mesosphere.

Kelsey Hightower and Chris Gaun on Serverless and Kubernetes

At Google’s 2019 Cloud Next conference, I sat down with Stephen Thorne, site reliability engineer on Google’s customer reliability engineering team and co-author of "The Site Reliability Workbook," to talk about how organizations, both large and small, can use SRE to reduce operational costs, improve reliability, and create productive cross-functional teams.

How to Get Started With Site Reliability Engineering (SRE)

In this interview, Tim Craig and fellow Googler Gustavo Franco, a site reliability engineer (SRE), discuss the wide range of events that qualify as “incidents;” the need for a conscious, robust, and well-defined process for understanding them; the role of training; and how to get buy-in from management so you can spread incident response training throughout an organization.

Taming Chaos: Preparing for Your Next Incident

Jon Bruner and Mike Loukides talk about the classical music publishing industry and how it's being disrupted by projects like the Petrucci Music Library (http://imslp.org/) that make public-domain scores available online.

The future of classical music

Jim Stogdill, Edd Dumbill and Roger Magoulas have a conversation about in-memory databases and the impact on real time analysis. Leave questions and comments for us at http://radar.oreilly.com/2013/05/talking-about-in-memory.html

Talking about in-memory

Jon Bruner, Mike Loukides and Jim Stogdill from O'Reilly Radar muse on the return of software to the physical world.

Software and the physical world

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about anthropology in big data, advances in JavaScript, searching for ancient civilizations, the importance of context, and good sites for checking the weather.

Related blog post: http://radar.oreilly.com/2013/06/radar-podcast-anthropology-big-data-and-the-importance-of-context.html

Anthropology, big data, and the importance of context

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about what's been on their minds this week: the NSA's surveillance programs, what defense contractors will do with their technology as defense budgets dry up, and a Californian who isn't doing what you think he's doing with hydroponics.

For more on these topics, and links to the things we talked about, see http://radar.oreilly.com/2013/06/radar-podcast-the-internet-of-things-prism-and-defense-technology-that-goes-civilian.html.

The Internet of Things, PRISM, and defense technology that goes civilian

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.