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.

O'Reilly Media Podcast

orielly

work

Podcast

O'REILLY

O'Reilly's Ben Lorica chats with Poppy Crum, neuroscientist and researcher at Dolby Labs, about AI and virtual reality systems, and about building a team of researchers from diverse disciplines.

Understanding Neural Function and Virtual Reality

Laura Klein and Kate Rutter chat about what is wrong with personas and how we can make some important tweaks in order make them more useful for design and communication.

This podcast is a cross-post from the User’s Know podcast (http://www.usersknow.com/podcast), and is republished here with permission. The podcast also is available on iTunes (https://itunes.apple.com/us/podcast/what-is-wrong-ux-users-know/id980133198).

Tweaking Personas: Mastering the Hunt for the Ideal User

O'Reilly's Rachel Roumeliotis chats with Joost Visser, head of research at Software Improvement Group (SIG), about how a focus on quality helps to achieve the fastest possible schedules and lowest possible cost of development and maintenance.

Better Code is Cheaper

O'Reilly's Mary Treseler chats with designer Gretchen Anderson about using design as a force to improve our lives and about approaching design as an inclusive discipline.

Design for the Greater Good

Rachel Roumeliotis talks to Intel's Chuck Freedman and Iman Saleh about the data analytics applications of the Trusted Analytics Platform (TAP).

The advantages of open source in analytics

Eliot Knudsen, Field Architect at Tamr, on the business value of prescriptive analytics and machine learning algorithms.

Machine learning for procurement analytics

In this episode of the Hardware Podcast, we talk with Rob Chandhok, president of Helium, a startup with a platform for connecting sensors and control elements over the Internet. We talk about the process of taking a prototype to production, and the blend of hardware and software that allows smart edge devices to last an eternity on a single battery.

Rob Chandhok on Connecting Sensors to the Internet

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap.

Marc Warner on AI's Fundamental Shift: Supplemental Thinking

Collaboration tools in the open-source Trusted Analytics Platform (TAP) address the human side of data analytics. O'Reilly's Rachel Roumeliotis speaks with Chuck Freedman, Chief Developer Advocate for TAP. 

Making data analytics easier for data scientists, developers, and DevOps

With the release of Spark version 2.0, streaming starts becoming much more accessible to users. By adopting a continuous processing model (on an infinite table), the developers of Spark have enabled users of its SQL or DataFrame APIs to extend their analytic capabilities to unbounded streams.

Within the Spark community, Databricks Engineer, Michael Armbrust is well-known for having led the long-term project to move Spark’s interactive analytics engine from Shark to Spark SQL. (Full disclosure: I’m an advisor to Databricks.) Most recently he has turned his efforts to helping introduce a much simpler stream processing model to Spark Streaming (“structured streaming”).

Structured Streaming Comes to Apache Spark 2.0

This week, O'Reilly's Mac Slocum chats with Ben Lorica, O'Reilly's chief data scientist and host of the O'Reilly Data Show Podcast. Lorica talks about emerging themes in the data space, from machine learning to deep learning to artificial intelligence, and how those technologies relate to one another and how they're fueling real-time data applications. Lorica also talks about how the concept of a data center is evolving, the importance of open source big data components, and the rise in interest of big data ethics.

Ben Lorica on the Emergence of Intelligent Real-Time Data Applications

In this episode of the O’Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies.

Building Intelligent Applications with Deep Learning and TensorFlow

I recently talked to Sean Suchter, co-founder and CEO of Pepperdata, about why Spark has become so popular and where it still presents challenges. Spark represents the evolution of how the computer field understands and is addressing the challenge of handling big data (a term I will casually use without trying to define—any definition you care to plug in will be relevant for this discussion).

Sean Suchter on the promise and challenges of Spark

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

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

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.