The O'Reilly Design Podcast explores how experience design -- and experience designers -- are shaping business, the Internet of Things, and other domains.

O'Reilly Design Podcast

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

The O'Reilly Security Podcast examines the challenges and opportunities for security practitioners, with a focus on the people on the frontlines of security, working to build better defenses.

O'Reilly Security Podcast

The O’Reilly Programming podcast features conversations that give developers, engineers, and architects tips on getting projects done better and faster.

O'Reilly Programming Podcast

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

O'Reilly's Jenn Webb chats with Cait O’Riordan, VP of product, music and platforms at Shazam, about the current state of predictive analytics and how Shazam is able to predict the success of a song, and how the Internet of Things affects Shazam’s product life cycles as well as the behaviors of their users. Jenn also chats with Francine Bennett, CEO and cofounder of Mastodon C, about unintentional uses of data for evil and how data scientists can ensure their work stays on the data for good side of that line.

How Shazam Predicts Pop Hits

O'Reilly's Mike Hendrickson chats with Autodesk research fellow Mickey McManus about engaging with extreme users and what's going to happen when we have trillions of things sending billions of messages. McManus also talked about how we can prepare for the coming era of unbounded malignant complexity.

Trillions of Things Sending Billions of Messages

Dr. Clare Bernard, a field engineer at Tamr, talks about integrating, cataloging, and preserving metadata.

Clare Bernard on Discovering, Organizing, and Visualizing Enterprise Data

Host and O'Reilly chief data scientist Ben Lorica interviews Nidhi Aggarwal, whose background is in hardware and software, and whose experience ranges from work in engineering, consulting, research, and entrepreneurship.

Ask Your Data New Questions

In this episode of the O’Reilly Podcast, O’Reilly’s Ben Lorica sat down with Ben Sharma, CEO and co-founder of Zaloni, an organization that provides enterprise data management solutions for Hadoop. They discussed real-time data processing, the changing nature of data, sentiment analysis, and the trend toward combining cloud with on-prem infrastructures.


Approaching Big Data From a Business Perspective

This week, O'Reilly's Mary Treseler chats with designer, creative coder, and artist Scott Murray about coding and computation in design, his book "Interactive Data Visualization for the Web" and his new book coming out soon "Creative Coding and Data Visualization with p5.js."

Designing With Code and Computation

In this episode, I talk with Jessy Irwin, VP of security and privacy at Mercury Public Affairs. We discuss how to communicate security to non-technical people, what security might look like for small businesses, and moving beyond shame. We also meet her neighborhood gang of grannies who’ve learned how to hack back.

Jessy Irwin on Making Security Understandable for Everyone

This week, I sit down with Travis Lowdermilk senior UX designer at Microsoft, and Jessica Rich, UX researcher at Microsoft; Lowdermilk and Rich are also co-authors of the "Customer Driven Playbook." We talk about why failing fast is not always a good approach, sensemaking, and never losing track of the customer’s voice.

Travis Lowdermilk and Jessica Rich on Building a Customer-Driven Culture at Microsoft

In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart. The objective is to get fresh groceries from popular retailers delivered to customers in a timely fashion. Instacart’s goals land them in the center of the many opportunities and challenges involved in building high-impact data products.

Data Science and Deep Learning in Retail

This week, I sit down with Cynthia Savard Saucier, director of design at Shopify and author of Tragic Design. Saucier also is keynoting at Velocity in New York, October 1-4, 2017. We talk about moving from working in design to leading designers, the real and sometimes negative impact that design decisions can have on users, and how design is organized at Shopify.

Cynthia Savard Saucier on Design at Shopify

In this week’s Design Podcast, I sit down with Cheryl Platz, senior designer at Microsoft for the Azure Portal and Marketplaces. We talk about the challenges of working on a top-secret design project, the research behind Amazon's Echo Look, the skills you need to start designing for voice, and how studying improv can make you a better designer.

Cheryl Platz on Designing the Amazon Echo Look

In this week’s Design Podcast, I sit down with John Whalen, chief experience officer at 10 Pearls, a digital development company focused on mobile and web apps, enterprise solutions, cyber security, big data, IoT,  and cloud and dev ops. We talk about the “six minds” that underlie each human experience, why it’s important for designers to understand brain science, and what people really look for in a voice assistant.

John Whalen on Using Brain Science in Design

In this week’s Design Podcast, I sit down with Julie Stanford, founder and principal of user experience agency Sliced Bread Design. We talk about how to get in the rapid experimentation mindset, the design thinking process, and how to get started with rapid experimentation at your company. Hint: start small.

Julie Stanford on Vetting Designs Through Rapid Experimentation

In this episode of the O’Reilly Programming Podcast, I talk with Jim Blandy and Jason Orendorff, both of Mozilla, where Blandy works on Firefox’s web developer tools and Orendorff is the module owner of Firefox’s JavaScript engine. They are the authors of the new O’Reilly book "Progamming Rust."

Jim Blandy and Jason Orendorff on Rust

In this episode of the Security Podcast, I talk with Andrea Limbago, chief social scientist at Endgame. We discuss how the misperception of security as a computer science skillset ultimately restricts innovation, the need to make security easier and accessible for everyone, and how current branding of security can discourage newcomers.

Andrea Limbago on the Effects of Security's Branding Problem

In this episode of the O’Reilly podcast, I speak with Han Yang, senior product manager at Cisco, working on analytics solutions. We discuss the impact of data analytics across industries, building data lifecycles for Internet of Things (IoT) applications, and advice for establishing successful analytics and machine learning projects.

Building Data Lifecycles for IoT Projects With Edge Analytics

In this episode of the Security Podcast, I talk with Charles Givre, senior lead data scientist at Orbital Insight. We discuss how data science skills are increasingly important for security professionals, the critical role of data scientists in making the results of their work accessible to even nontechnical stakeholders, and using machine learning as a dynamic filter for vast amounts of data.

Charles Givre on the Impetus for Training All Security Teams in Basic Data Science

In this episode of the O’Reilly Podcast, I speak with Amit Vij, CEO and co-founder of Kinetica, a company that has developed an analytics database that uses graphics processing units (GPUs). We talk about how organizations are using GPU-accelerated databases to converge artificial intelligence (AI) and business intelligence (BI) on a single platform.

Amit Vij on GPU-Accelerated Analytics Databases

In this episode of the Security Podcast, O’Reilly’s Mac Slocum talks with Susan Sons, senior systems analyst for the Center for Applied Cybersecurity Research (CACR) at Indiana University. They discuss how she initially got involved with fixing the open source Network Time Protocol (NTP) project, recruiting and training new people to help maintain open source projects like NTP, and how security needn’t be an impediment to organizations moving quickly.

Susan Sons on Building Security from First Principles

In this episode of the Security Podcast, I talk with Christie Terrill, partner at Bishop Fox. We discuss the importance of educating businesses on the complexities of “being secure,” how to approach building a strong security program, and aligning security goals with the larger processes and goals of the business.

Christie Terrill on Building a High-Caliber Security Program in 90 Days

In a recent episode of the O’Reilly Media Podcast, David Hsieh, senior vice president of marketing at Qubole, sat down with John Slocum, vice president of MediaMath’s data management platform (DMP), to discuss DataOps in the media industry. “DataOps” refers to the promotion of communication between formerly siloed data, teams, and systems. As discussed in "Creating a Data-Driven Enterprise with DataOps," a report published by Qubole and O’Reilly in 2016, DataOps leverages process change, organizational realignment, and technology to facilitate relationships between everyone who handles data: developers, data engineers, data scientists, analysts, and business users. As a programmatic advertising platform, MediaMath has a unique lens into the shifting business models across the media industry, and how DataOps is playing a role in those shifts.

During the podcast, Hsieh and Slocum discussed how data has transformed the culture and overall goals of organizations in the media industry in the past 10 years, and shared some best practices for companies that are just embarking on their journey toward becoming data driven.

What It Means to Be Data-Driven in the Media Industry

In this episode of the O’Reilly Media Podcast, I spoke with Gayle Sheppard, vice president and general manager of Saffron AI Group at Intel, and David Thomas, chief analytics officer for Bank of New Zealand (BNZ). Our conversations centered around the utility of artificial intelligence in the financial services industry.

Associative Memory AI: Enabling Machines to Fight Financial Crime With Human-Like Reasoning

Taking Banking Into the Future With Lean Analytics and AI

In this episode of the Security Podcast, I talk with Rich Smith, director of labs at Duo Labs, the research arm of Duo Security. We discuss the goals of agile application security, how to reframe success for security teams, and the short- and long-term implications of your security culture.

Rich Smith on Redefining Success for Security Teams and Managing Security Culture

In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify. Prior to joining Spotify, she led data teams at the NY Times and at Apple (iTunes). Having led teams at three different companies, I wanted to hear her thoughts on digital transformation, and I wanted to know how she approaches the challenge of building, managing, and nurturing data teams.

I also wanted to learn more about what goes into building a recommender system for a popular consumer service like Spotify. Engagement should clearly be the most important metric, but there are other considerations, such as introducing users to new or “long tail” content.

Machine Learning at Spotify: You Are What You Stream

In this episode of the O’Reilly Programming Podcast, I talk with Paul Bakker, senior software engineer on the edge developer experience team at Netflix, and Sander Mak, a fellow at Luminis Technologies. They are the authors of the O’Reilly book "Java 9 Modularity," in which they call the introduction of the module system to the platform “the start of a new era.”

Paul Bakker and Sander Mak on Java 9 Modularity

In this O’Reilly Podcast, Rachel Roumeliotis, vice president of content strategy at O’Reilly Media, speaks with Atif Kureishy, global VP of emerging practices and artificial intelligence and deep learning at Teradata, a data and analytics company.  They discuss how enterprises are currently investing in artificial intelligence (AI), which industries are seeing the most impact, barriers to AI implementation, and future trends.

Atif Kureishy on How Enterprises are Investing in AI

In this episode of the O’Reilly Programming Podcast, I talk with Richard Warburton and Raoul-Gabriel Urma of Iteratr Learning. They are the presenters of a series of O’Reilly Learning Paths, including "Getting Started with Reactive Programming" and "Build Reactive Applications in Java 8." Warburton is the author of "Java 8 Lambdas," and Urma is the author of "Java 8 in Action."

Richard Warburton and Raoul-Gabriel Urma on Java 8 and Reactive Programming

In this episode of the O’Reilly Programming Podcast, I talk about Jenkins 2 and Git with Brent Laster, who presents a number of live online training courses on these topics (including "Building a deployment pipeline with Jenkins 2," and "Next level Git"). Laster will also present the workshop "Power Git" at the O’Reilly Open Source Convention, July 16-19, 2018, in Portland, Oregon, and he is the author of the forthcoming O’Reilly book "Jenkins 2: Up and Running."

Brent Laster on Jenkins 2 and Git

Modern-day DNS goes beyond a simple internet “phone book” service to provide dynamic traffic management, flexibility, and performance. In this episode of the O’Reilly podcast, I had a chance to discuss modern-day DNS and its role in building resilient infrastructure with Gary Sloper, VP of global sales engineering at Dyn.

Gary Sloper on the Evolving Role of DNS in Building Resilient Infrastructure

In this episode of the O’Reilly Podcast, I talk about issues surrounding stateful containers and services with Tobi Knaup, CTO and co-founder of Mesosphere, whose DC/OS is a platform for deploying stateful and stateless applications on any combination of infrastructure, and Gou Rao, CTO of Portworx, a provider of persistent storage for containers.

Tobi Knaup and Gou Rao on Stateful Containers

In this episode of the O’Reilly Programming Podcast, I talk about Kubernetes, containers, and more with Bridget Kromhout, a principal cloud developer advocate at Microsoft, and a frequent speaker at tech conferences. She will be leading the workshop "Kubernetes 101" at the O’Reilly Velocity Conference in San Jose, June 11-14, 2018, and at the O’Reilly Open Source Convention (OSCON), July 16-19, 2018.

Bridget Kromhout on Kubernetes

In this episode of the O’Reilly Programming Podcast, I talk with two of the program chairs for the upcoming O’Reilly Fluent Conference (July 11-14 in San Jose), Kyle Simpson and Tammy Everts. Simpson is co-author of the "HTML 5 Cookbook," and the author of the "You Don’t Know JS" series of books. Everts is the chief experience officer at SpeedCurve and the author of "Time is Money: The Business Value of Web Performance."

Kyle Simpson and Tammy Everts on the Challenges of the Modern Web

In this episode of the O’Reilly Podcast, I talk with Simon Moss, vice president of industry consulting and solutions, Americas, at Teradata. We discuss how machine learning and deep learning techniques are being used to fight financial crimes, such as credit card fraud, identity theft, health care fraud, and money laundering.

Simon Moss on Using Artificial Intelligence to Fight Financial Crimes

In this episode of the O’Reilly Podcast, I talk with Brendan Eich, the creator of JavaScript, co-founder of the Mozilla Project and Foundation, and CEO and founder of Brave Software. Eich will be a keynote speaker at the upcoming O’Reilly Fluent Conference, July 11-14, 2018, in San Jose.

The Web Is Broken; Lets Fix It

In this episode of the O’Reilly Media Podcast, I talk with JP Phillips, platform engineer at IBM Cloud. IBM is driving development in the container space, as shown through last year’s launch of Istio, an open cloud service that allows developers to connect, manage, and secure networks of different microservices. Istio, a joint collaboration between IBM, Google, and Lyft, (in conjunction with the Cloud Native Computing Foundation), is platform agnostic and runs on Kubernetes platforms, such as IBM’s Cloud Container Service. Interest in the platform is growing. IBM's Daniel Berg, who is giving a talk on Istio at the upcoming OSCON conference in Portland, recently led a hands-on workshop at KubeCon in Copenhagen to help developers learn how Istio can solve common challenges with microservices deployed within Kubernetes.

Kubernetes and Containers for Enterprise Developers

In this episode of the O’Reilly Podcast, I talk with Tammy Butow, a site reliability engineer at Gremlin, and Annie Lau, a software engineering manager at Trulia, about creating a culture of learning, how experimentation is important to business, and their careers in tech.

From Chaos Engineering to Bug Bounties—Why Companies Need to Experiment with New Technology

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 episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines.

Enabling End-to-End Machine Learning Pipelines in Real-World Applications

In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).

Tools for Machine Learning Development

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

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

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.

Machine Learning and Analytics for Time Series Data

In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.

Machine Learning for Operational Analytics and Business Intelligence

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.

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