Oreilly

O'Reilly

Listened

data stuff

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 Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.

Data Science Makes an Impact on Wall Street

In this episode of the O’Reilly Data Show, I spoke with John Akred, cofounder and CTO of Silicon Valley Data Science. Akred and his colleagues teach two of the more popular Strata + Hadoop World tutorials—“Developing a Modern Enterprise Data Strategy” and “Architecting a Data Platform.” We talked about his career in data science and consulting, and his penchant for bringing emerging technologies and tools into large enterprises.

Using Agile Development Techniques for Data Science Projects

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I look back at 2016, a big year for bots that saw important developments in platforms, tools, and underlying AI.

2016 Bots Year in Review

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I speak with Brad Abrams, group product manager of Google Assistant, the company’s new AI-driven bot that lives in many different contexts, including the Pixel phone, the Allo messaging app, and the Google Home voice-controlled speaker.

Brad Abrams on Google Assistant

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I speak with Amir Shevat, head of developer relations at Slack and the author of the forthcoming O’Reilly book "Designing Bots: Creating Conversational Experiences."

Amir Shevat on Enterprise Workplace Communication

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

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I talk to Jason Laska and Michael Akilian of Clara Labs, creator of a virtual assistant—Clara—that schedules meetings and interacts in natural language through email.

E-mail is, to me, a highly promising (and somewhat underrated) venue for bots. Messaging is growing quickly, but e-mail is still the standard way to communicate within businesses and especially between businesses. E-mail conventions are somewhat standardized, and much of it is highly routinized—automatically generated reports, receipts, etc.—so it’s ripe for automation.

Laska, who leads the machine learning efforts at Clara Labs, and Akilian, the company’s co-founder and CTO, talk about the reality of developing an AI-driven product, and explain Clara’s human-in-the-loop system. “People are still there to do some of the most challenging aspects of this work, and that’s exactly what you want to use people for,” says Laska.

Jason Laska and Michael Akilian on Scheduling Bots

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 episode of the Data Show, I spoke with Geoffrey Bradway, VP of engineering at Numerai, a new hedge fund that relies on contributions of external data scientists. The company hosts regular competitions where data scientists submit machine learning models for classification tasks. The most promising submissions are then added to an ensemble of models that the company uses to trade in real-world financial markets.

Programming Collective Intelligence for Financial Trading

In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware. Through his startup Nervana (since acquired by Intel), Rao has been at the forefront of building a next generation platform for deep learning and AI.

I wanted to get his thoughts on what the future infrastructure for machine learning would look like. At least for now, we’re seeing a variety of approaches, and many companies are using heterogeneous processors (even specialized ones) and proprietary interconnects for deep learning. Nvidia and Intel Nervana are set to release processors that excel at both training and inference, but as Rao pointed out, at large-scale there are many considerations—including utilization, power consumption, and convenience—that come into play.

Building a Next-Generation Platform for Deep Learning

In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed recent developments, including its applications to reinforcement learning problems.

Effective Mechanisms for Searching the Space of Machine Learning Algorithms

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.

STRATA DATA CONFERENCE


Strata Data Conference in San Jose, March 5-8, 2018
Registration is now open 
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 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

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.

Programming Collective Intelligence for Financial Trading

O'Reilly Radar