"Big Data" entries

Showcasing the real-time processing revival

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

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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…

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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…

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Data-driven neuroscience

The O'Reilly Radar Podcast: Bradley Voytek on data's role in neuroscience, the brain scanner, and zombie brains in STEM.

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Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

In this week’s Radar Podcast, O’Reilly’s Mac Slocum chats with Bradley Voytek, an assistant professor of cognitive science and neuroscience at UC San Diego. Voytek talks about using data-driven approaches in his neuroscience work, the brain scanner project, and applying cognitive neuroscience to the zombie brain.

Here are a few snippets from their chat:

In the neurosciences, we’ve got something like three million peer reviewed publications to go through. When I was working on my Ph.D., I was very interested, in particular, in two brain regions. I wanted to know how these two brain regions connect, what are the inputs to them and where do they output to. In my naivety as a Ph.D. student, I had assumed there would be some sort of nice 3D visualization, where I could click on a brain region and see all of its inputs and outputs. Such a thing did not exist — still doesn’t, really. So instead, I ended up spending three or four months of my Ph.D. combing through papers written in the 1970s … and I kept thinking to myself, this is ridiculous, and this just stewed in the back of my mind for a really long time.

Sitting at home [with my wife], I said, I think I’ve figured out how to address this problem I’m working on, which is basically very simple text mining. Lets just scrape the text of these three million papers, or at least the titles and abstracts, and see what words co-occur frequently together. It was very rudimentary text mining, with the idea that if words co-occur frequently … this might give us an index of how related things are, and she challenged me to a code-off.

Read more…

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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…

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A “bottom-up” approach to data unification

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

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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…

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Four short links: 18 August 2015

Four short links: 18 August 2015

Chris Grainger Ships, Disorderly Data-Centric Languages, PCA for Fun and Fashion, and Know Thy History

  1. Eve, Version 0 (Chris Grainger) — Version 0 contains a database, compiler, query runtime, data editor, and query editor. Basically, it’s a database with an IDE. You can add data both manually or through importing a CSV and then you can create queries over that data using our visual query editor.
  2. BOOM: Berkeley Orders Of Magnitudean effort to explore implementing Cloud software using disorderly, data-centric languages.
  3. Eigenstyle — clever analysis and reconstruction of images through principal component analysis. And here are “prettiest ugly dresses,” those that I classified as dislikes, that the program predicted I would really like.
  4. Turing Digital Archivemany of Turing’s letters, talks, photographs, and unpublished papers, as well as memoirs and obituaries written about him. It contains images of the original documents that are held in the Turing collection at King’s College, Cambridge. (Timely as Jason Scott works to save a manual archive: [1], [2], [3])
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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

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…

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We make the software, you make the robots

An interview with Andreas Mueller, on scikit-learn and usable machine learning software.

Get notified when our free report “Evaluating Machine Learning Models: A beginner’s guide to key concepts and pitfalls,” by Alice Zheng, is available for download.

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Superpixels example from Andreas Mueller’s thesis paper (PDF), used with permission.

A few weeks ago, I had the pleasure of sitting down (virtually, over Skype) with Andreas Mueller, core developer and maintainer of the popular scikit-learn machine learning library. We had previously bonded over our shared goals of making useful machine learning software, so I jumped at the chance to interview him.

Mueller wears many hats at work. He is one of the key maintainers of the popular Python machine learning library scikit-learn. Holding a doctorate in computer vision from the University of Bonn in Germany, he currently works on open science at New York University’s Center for Data Science. He speaks at conferences around the world and has a fanbase of 5,000+ followers on Twitter and about as many reputation points on Stack Overflow. In other words, this man has got mad street cred. He started out doing pure math in academia, and has now achieved software developer cult idol status. Read more…

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Four short links: 11 August 2015

Four short links: 11 August 2015

Real-time Sports Analytics, UI Regression Testing, AI vs. Charity, and Google's Data Pipeline Model

  1. Denver Broncos Testing In-Game Analytics — their newly hired director of analytics working with the coach. With Tanney nearby, Kubiak can receive a quick report on the statistical probabilities of almost any situation. Say that you have fourth-and-3 from the opponent’s 45-yard-line with four minutes to go. Do the large-sample-size percentages make the risk-reward ratio acceptable enough to go for it? Tanney’s analytics can provide insight to aid Kubiak’s decision-making. (via Flowing Data)
  2. Visual Review (GitHub) — Apache-licensed productive and human-friendly workflow for testing and reviewing your Web application’s layout for any regressions.
  3. Effective Altruism / Global AI (Vox) — fear of AI-run-amok (“existential risks”) contaminating a charity movement.
  4. The Dataflow Model (PDF) — Google Research paper presenting a model aimed at ease of use in building practical, massive-scale data processing pipelines.
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The world beyond batch: Streaming 101

A high-level tour of modern data-processing concepts.

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Editor’s note: This is the first post in a two-part series about the evolution of data processing, with a focus on streaming systems, unbounded data sets, and the future of big data.

Streaming data processing is a big deal in big data these days, and for good reasons. Amongst them:

  • Businesses crave ever more timely data, and switching to streaming is a good way to achieve lower latency.
  • The massive, unbounded data sets that are increasingly common in modern business are more easily tamed using a system designed for such never-ending volumes of data.
  • Processing data as they arrive spreads workloads out more evenly over time, yielding more consistent and predictable consumption of resources.

Despite this business-driven surge of interest in streaming, the majority of streaming systems in existence remain relatively immature compared to their batch brethren, which has resulted in a lot of exciting, active development in the space recently.

As someone who’s worked on massive-scale streaming systems at Google for the last five+ years (MillWheel, Cloud Dataflow), I’m delighted by this streaming zeitgeist, to say the least. I’m also interested in making sure that folks understand everything that streaming systems are capable of and how they are best put to use, particularly given the semantic gap that remains between most existing batch and streaming systems. To that end, the fine folks at O’Reilly have invited me to contribute a written rendition of my Say Goodbye to Batch talk from Strata + Hadoop World London 2015. Since I have quite a bit to cover, I’ll be splitting this across two separate posts:

  1. Streaming 101: This first post will cover some basic background information and clarify some terminology before diving into details about time domains and a high-level overview of common approaches to data processing, both batch and streaming.
  2. The Dataflow Model: The second post will consist primarily of a whirlwind tour of the unified batch + streaming model used by Cloud Dataflow, facilitated by a concrete example applied across a diverse set of use cases. After that, I’ll conclude with a brief semantic comparison of existing batch and streaming systems.

So, long-winded introductions out of the way, let’s get nerdy. Read more…

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