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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

Obama's Affordable Care Act promotes the digitization of millions of medical records to measure outcomes and contain costs. Big Data may also help doctors better understand many diseases, who's most likely to get them and what the best treatments might be. It also makes the most intimate kind of personal information available to the government, insurance and drug companies — even prospective employers.  We look at the pros and cons.
Warren Olney hosts To the Point with guests, Joel Dudley: Mount Sinai Hospital  Nortin Hadler: University of North Carolina at Chapel Hill, Iya Khalil: GNS Healthcare and Deborah Peel: Patient Privacy Rights

To the Point: Big Data and Healthcare

How can ecommerce companies deal with the conflicting expectations of consumers and logistics realities of an on-demand economy? How can they compete with ecommerce giant Amazon, which just gets bigger every year? And given all this, how is physical retail faring?

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Ecommerce and the Holiday Shopping Collision

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

John Follett, editor of "Designing for Emerging Technologies" sits down with Dirk Knemeyer, founder of Involution Studios, who contributed to the book. Knemeyer discusses the changing role of design and designers in emerging technology.

Designers are Engineers

O'Reilly's Mary Treseler sits down with Jeff Veen, vice president of products at Adobe, and CEO and founder of Typekit to talk about leadership, hiring designers, and the significance of trends.

Pace Of Change As A Metaphor For Full Stack Design

O'Reilly Ben Lorica chats with Ion Stoica, UC Berkeley Professor and Databricks CEO, about the rise of Apache Spark and Apache Mesos.

Apache Spark's Journey From Academia To Industry

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

O'Reilly's Ben Lorica talks with Mikio Braun about machine learning, stream processing and analytics, his recent foray into data science training, and academia versus industry.

Building Big Data Systems in Academia and Industry

"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it's become a buzzword that captures so many things)... 

On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe 'big data' in relation to 'machine learning' -- as well as what comes next with 'deep learning'. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

O'Reilly's Ben Lorica chats with Anima Anandkumar about tensor decomposition techniques for machine learning.

The Tensor Renaissance In Data Science

O'Reilly's Mike Hendrickson chats with database pioneer and 2014 Turing Award recipient Michael Stonebraker about winning the award, the future of data science, and the importance — and difficulty — of data curation.

The Future of Data at Scale

Google engineer Tyler Akidau on the evolution of stream processing, the challenges of building systems that scale to massive data sets, and the recent surge in interest in all things real time.

Building Systems for Massive Scale Data Applications

O’Reilly’s Mary Treseler chats with Aaron Irizarry, director of user experience for Nasdaq product design, about Nasdaq’s journey to become a design-driven organization.

Irizarry also talks about the best ways to have solid conversations about the designs you’re working on, and why getting a seat at the proverbial table isn’t the endgame.

Aaron Irizarry on Nasdaq's Journey to Embrace Product Design

Jai Ranganathan, senior director of product management at Cloudera, on trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends

Jai Ranganathan On Architecting Big Data Applications In The Cloud

Mike Kuniavsky, a principal scientist in the Innovation Services Group at PARC, on designing for the Internet of Things ecosystem and why the most interesting thing about the IoT isn’t the “things” but the sensors. He also talks about his deep-seated love for appliances and furniture, and how intelligence will affect those industries.

Mike Kuniavsky on the Tectonic Shift of the IoT

In episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning, the language made of shapes of words, and New England Machine Learning Day. We take a look at the workshops being presented at NIPS this year, and we take a listener question about changing the number of features your data has.

Workshops At NIPS And Crowdsourcing In Machine Learning

Vanessa Cho, head of UX and research for the software and services group at GoPro, on designing for hardware and software, building design teams, and what she looks for in new recruits.

Vanessa Cho on GoPro's Design Approach

Kristian Hammond, Narrative Science’s chief scientist, on Natural Language Generation, Narrative Science’s shift into the world of business data, and evolving beyond the dashboard.

Kristian Hammond on Truly Democratizing Data and the Value of AI in the Enterprise

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.

In the second segment, Reynold Xin, one of the architects of Apache Spark, explains the rise of Apache Spark in China.

Investing in Big Data Technologies

In this O’Reilly Podcast, Ben Lorica talks with John Hugg, founding engineer and manager of developer outreach at VoltDB. From the deluge of today's diverse data, Hugg describes how to extract meaningful information to make things cheaper and faster.

Streaming data + stream processing = information

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.).

Stream Processing and Messaging Systems for the IoT Age

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 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

2013 SVA Commencement Address. 

Transcript, highlights, and discussion: http://www.brainpickings.org/index.php/2013/05/13/greil-marcus-sva-commencement-address/

Greil Marcus on the artificial divide between "high" and "low" art

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.