Showcasing the real-time processing revival

Tools and learning resources for building intelligent, real-time products.

Earth orbiting sun illustration

Register for Strata + Hadoop World NYC, which will take place September 29 to Oct 1, 2015.

A few months ago, I noted the resurgence in interest in large-scale stream-processing tools and real-time applications. Interest remains strong, and if anything, I’ve noticed growth in the number of companies wanting to understand how they can leverage the growing number of tools and learning resources to build intelligent, real-time products.

This is something we’ve observed using many metrics, including product sales, the number of submissions to our conferences, and the traffic to Radar and newsletter articles.

As we looked at putting together the program for Strata + Hadoop World NYC, we were excited to see a large number of compelling proposals on these topics. To that end, I’m pleased to highlight a strong collection of sessions on real-time processing and applications coming up at the event. Read more…


Bridging the divide: Business users and machine learning experts

The O'Reilly Data Show Podcast: Alice Zheng on feature representations, model evaluation, and machine learning models.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

606px-IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881As tools for advanced analytics become more accessible, data scientist’s roles will evolve. Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the reality is that expertise in advanced algorithms is just one aspect of industrial data science.

During the latest episode of the O’Reilly Data Show podcast, I sat down with Alice Zheng, one of Strata + Hadoop World’s most popular speakers. She has a gift for explaining complex topics to a broad audience, through presentations and in writing. We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

Making machine learning accessible

People who work at getting analytics adopted and deployed learn early on the importance of working with domain/business experts. As excited as I am about the growing number of tools that open up analytics to business users, the interplay between data experts (data scientists, data engineers) and domain experts remains important. In fact, human-in-the-loop systems are being used in many critical data pipelines. Zheng recounts her experience working with business analysts:

It’s not enough to tell someone, “This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.” As a builder of these applications, you need to understand what the algorithm is doing in order to make it better. As a user who ultimately consumes the results, it can be really frustrating to not understand how they were produced. When we worked with analysts in Windows or in Bing, we were analyzing computer system logs. That’s very difficult for a human being to understand. We definitely had to work with the experts who understood the semantics of the logs in order to make progress. They had to understand what the machine learning algorithms were doing in order to provide useful feedback. Read more…


ResourceMiner: Toppling the Tower of Babel in the lab

An open source project aims to crowdsource a common language for experimental design.


Contributing author: Tim Gardner

Editor’s note: This post originally appeared on PLOS Tech; it is republished here with permission.

From Gutenberg’s invention of the printing press to the Internet of today, technology has enabled faster communication, and faster communication has accelerated technology development. Today, we can zip photos from a mountaintop in Switzerland back home to San Francisco with hardly a thought, but that wasn’t so trivial just a decade ago. It’s not just selfies that are being sent; it’s also product designs, manufacturing instructions, and research plans — all of it enabled by invisible technical standards (e.g., TCP/IP) and language standards (e.g., English) that allow machines and people to communicate.

But in the laboratory sciences (life, chemical, material, and other disciplines), communication remains inhibited by practices more akin to the oral traditions of a blacksmith shop than the modern Internet. In a typical academic lab, the reference description of an experiment is the long-form narrative in the “Materials and Methods” section of a paper or a book. Similarly, industry researchers depend on basic text documents in the form of Standard Operating Procedures. In both cases, essential details of the materials and protocol for an experiment are typically written somewhere in a long-forgotten, hard-to-interpret lab notebook (paper or electronic). More typically, details are simply left to the experimenter to remember and to the “lab culture” to retain.

At the dawn of science, when a handful of researchers were working on fundamental questions, this may have been good enough. But nowadays this archaic method of protocol record keeping and sharing is so lacking that half of all biomedical studies are estimated to be irreproducible, wasting $28 billion each year of U.S. government funding. With more than $400 billion invested each year in biological and chemical research globally, the full cost of irreproducible research to the public and private sector worldwide could be staggeringly large. Read more…


Unsupervised learning, attention, and other mysteries

How to almost necessarily succeed: An interview with Google research scientist Ilya Sutskever.

Get notified when our free report “Future of Machine Intelligence: Perspectives from Leading Practitioners” is available for download. The following interview is one of many that will be included in the report.

633px-Jan_Steen_-_A_School_for_Boys_and_Girls_-_Google_Art_ProjectIlya Sutskever is a research scientist at Google and the author of numerous publications on neural networks and related topics. Sutskever is a co-founder of DNNresearch and was named Canada’s first Google Fellow.

Key Takeaways:

  1. Since humans can solve perception problems very quickly, despite our neurons being relatively slow, moderately deep and large neural networks have enabled machines to succeed in a similar fashion.
  2. Unsupervised learning is still a mystery, but a full understanding of that domain has the potential to fundamentally transform the field of machine learning.
  3. Attention models represent a promising direction for powerful learning algorithms that require ever less data to be successful on harder problems.

David Beyer: Let’s start with your background. What was the evolution of your interest in machine learning, and how did you zero-in on your Ph.D. work?

Ilya Sutskever: I started my Ph.D. just before deep learning became a thing. I was working on a number of different projects, mostly centered around neural networks. My understanding of the field crystallized when collaborating with James Martens on the Hessian-free optimizer. At the time, greedy layer-wise training (training one layer at a time) was extremely popular. Working on the Hessian-free optimizer helped me understand that if you just train a very large and deep neural network on a lot of data, you will almost necessarily succeed. Read more…


A “bottom-up” approach to data unification

How machine learning plus expert sourcing can unify customer data at scale.


Watch the free webcast Integrating Customer Data at Scale to learn how Toyota Motor Europe was able to unify its customer data at scale.

Enterprises that are capable of gaining a unified view of their customer data can achieve added business enhancements and user opportunities. Capturing customer data, however, can be a difficult task, as most systems rely on traditional “top-down” approaches to standardizing data. In a recent O’Reilly webcast, Integrating Customer Data at Scale, Tamr field engineer Alan Wagner hosts a Q&A session with Matt Stevens, the general manager at Toyota Motor Europe, to demonstrate how a leading enterprise uses a third-generation system like Tamr to simplify the process of unifying customer data.

In the webcast, Stevens explains how Toyota Motor Europe has gained a 360-degree view of their customers through the Tamr Data Unification Platform, which takes a machine learning and expert-sourcing “human guided workflow” approach to data unification. Wagner provides a demo of the Tamr platform, applied within a Salesforce application, to demonstrate the ability to capture and unify customer data. Read more…


Pattern recognition and sports data

The O'Reilly Data Show Podcast: Award-winning journalist David Epstein on the (data) science of sports.

Sign-up now to receive a free download of the new O’Reilly report “Data Analytics in Sports: How Playing with Data Transforms the Game” when it publishes this fall.


Julien Vervaecke and Maurice Geldhof smoking a cigarette at the 1927 Tour de France. Public domain photo via Wikimedia Commons.

One of my favorite books from the last few years is David Epstein’s engaging tour through sports science using examples and stories from a wide variety of athletic endeavors. Epstein draws on examples from individual sports (including track and field, winter sports) and major U.S. team sports (baseball, basketball, and American football), and uses the latest research to explain how data and science are being used to improve athletic performance.

In a recent episode of the O’Reilly Data Show Podcast, I spoke with Epstein about his book, data science and sports, and his recent series of articles detailing suspicious practices at one of the world’s premier track and field training programs (the Oregon Project).

Nature/nurture and hardware/software

Epstein’s book contains examples of sports where athletes with certain physical attributes start off with an advantage. In relation to that, we discussed feature selection and feature engineering — the relative importance of factors like training methods, technique, genes, equipment, and diet — topics which Epstein has written about and studied extensively:

One of the most important findings in sports genetics is that your ability to improve with respect to a certain training program is mediated by your genes, so it’s really important to find the kind of training program that’s best tailored to your physiology. … The skills it takes for team sports, these perceptual skills, nobody is born with those. Those are completely software, to use the computer analogy. But it turns out that once the software is downloaded, it’s like a computer. While your hardware doesn’t do anything alone without software, once you’ve got the software, the hardware actually makes a lot of a difference in how good of an operating machine you have. It can be obscured when people don’t study it correctly, which is why I took on some of the 10,000 hours stuff. Read more…