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

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In this episode of the O’Reilly Data Show, O’Reilly’s online managing editor Jenn Webb speaks with Natalino Busa on the topic of predictive analytics, the challenges of feature engineering, and a new class of techniques that is enabling features to emerge from patterns within the data. They also discuss the relationship between predictive techniques and high-quality microservices, and how machine learning is being used to improve financial services.

Understanding Predictive Analytics

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 Data Show, I spoke with Francisco Webber, founder of Cortical.io, a startup that is applying tools based on Hierarchical Temporal Memory (HTM) to natural language understanding. While HTM has been around for more than a decade, there aren’t many companies that have released products based on it (at least compared to other machine learning methods). Numenta, an organization developing open source machine intelligence based on the biology of the neocortex, maintains a community site featuring showcase applications. Webber’s company has been building tools based on HTM and applying them to big text data in a variety of industries; financial services has been a particularly strong vertical for Cortical.

Natural Language Analysis Using Hierarchical Temporal Memory

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text. The absence of libraries meant rolling my own NLP utilities, and, in many cases, implementing a machine learning library (this was pre deep learning, and MLlib was much smaller). I’d always wondered why no one bothered to create an NLP library for Spark when many people were using Spark to process large amounts of text. The recent, early success of BigDL confirms that users like the option of having native libraries.

In this episode of the Data Show, I spoke with David Talby of Pacific.AI, a consulting company that specializes in data science, analytics, and big data. A couple of years ago I mentioned the need for an NLP library within Spark to Talby; he not only agreed, he rounded up collaborators to build such a library. They eventually carved out time to build the newly released Spark NLP library. Judging by the reception received by BigDL and the number of Spark users faced with large-scale text processing tasks, I suspect Spark NLP will be a standard tool among Spark users.

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Talby and I also discussed his work helping companies build, deploy, and monitor machine learning models. Tools and best practices for model development and deployment are just beginning to emerge—I summarized some of them in a recent post, and, in this episode, I discussed these topics with a leading practitioner.

Building a Natural Language Processing Library for Apache Spark

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 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

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.

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

O'Reilly Radar