How big compute is powering the deep learning rocket ship

The D.E. Shaw Supercomputer, Anton. (source: Matt Simmons on Flickr).For more on this topic, check out the deep learning session lineup at Strata + Hadoop World San Jose, March 13-16, 2017. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster. In recent…
Original Post: How big compute is powering the deep learning rocket ship

The 2017 machine learning outlook

Tower viewer. (source: Pexels).Join Steven Camiña of MemSQL for “Building the Ideal Stack for Machine Learning,” where he’ll share how to use real-time data for machine learning. Machine learning has been a mainstream commercial field for some time now, but it’s going through an important acceleration. In this podcast episode, I talk about that acceleration with two executives from MemSQL, a company that specializes in in-memory databases: Gary Orenstein, MemSQL chief marketing officer, and Drew Paroski, MemSQL vice president of engineering. Orenstein and Paroski identify a few crucial inflections in the machine learning landscape: machine learning models have become easier to write; computing capacity on the cloud has increased dramatically; and new sources of data—everything from drones to smart-home devices and industrial controllers—have added new richness to machine learning models. Computing capacity and software progress have made it possible to…
Original Post: The 2017 machine learning outlook

Fintech in 2017: 4 things to watch

Binoculars. (source: PublicDomainPictures.net).Get the O’Reilly Fintech Newsletter and receive crucial insights and news about the impact of technology on currency, transactions, and capitalism. The following piece was first published in the newsletter. Fintech companies large and small face many of the same disruptive trends as every other kind of tech company—especially the rise of artificial intelligence (AI) and uncertain political outlooks in the United States and Europe. Jon Bruner takes a look at what 2017 might hold in the fintech world. 1. The rise of Chinese fintech China’s eight fintech unicorns—privately held startups valued at more than $1 billion—are together valued at nearly $100 billion, three times as much as America’s 14 fintech unicorns. A couple of them are big because they’re tie-ups between several of China’s largest internet companies, but the breathtaking scale of China’s fintech market is still…
Original Post: Fintech in 2017: 4 things to watch

8 data trends on our radar for 2017

Radar screen (source: BenFrantzDale via Flickr).Get the O’Reilly Data Newsletter and receive weekly data news and insights from industry insiders. The following piece was first published in the Data newsletter. The data community will have plenty of opportunities in 2017—and a few gnarly challenges. Here’s a look at what lies ahead. 1. More data scientists will begin using deep learning. 2016 saw major advancements in deep learning and the release of new tools to make deep learning simpler, as well as tools that integrate directly with existing big data platforms and frameworks. And there are simply too many useful things you can do with deep learning—many that are becoming mission critical—for data scientists to avoid deep learning in 2017. Think times series and event data (including anomaly detection), IoT and sensor-related data analysis, speech recognition, and text mining recommenders, to…
Original Post: 8 data trends on our radar for 2017

2017 will be the year the data science and big data community engage with AI technologies

The Tulip Stairs and lantern at the Queen’s House in Greenwich by Inigo Jones. (source: Mcginnly on Wikimedia Commons).Strata + Hadoop World San Jose will take place March 13-16, 2017. Use code BIGDATA20 for a 20% discount on registration. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. This episode consists of excerpts from a recent talk I gave at a conference commemorating the end of the UC Berkeley AMPLab project. This section pertained to some recent trends in Data and AI. For a complete list of trends we’re watching in 2017, as well as regular doses of highly curated resources, subscribe to our Data and AI newsletters. As 2016 draws to a close, I see the big data and data…
Original Post: 2017 will be the year the data science and big data community engage with AI technologies

4 trends in security data science for 2017

Four square. (source: Pixabay).Get started with deep learning and neural networks with “Fundamentals of Deep Learning,” by Nikhil Buduma. Security data science is booming—reports indicate that the security analytics market is set to reach $8 billion dollars by 2023, with a growth rate of 26%, thanks to relentless cyber attacks. If you want to stay ahead of emerging security threats in 2017, it is important to invest in the right areas. In March 2016, I wrote a piece on the 4 trends to be aware of for 2016; for my 2017 trends post, Cody Rioux from Netflix joins me, bringing his platform perspective. Our goal is to help you formulate a plan for every quarter of 2017 (i.e., 4 trends for 4 quarters). For each of our trends, we provide a short rationale, why we think the time is right…
Original Post: 4 trends in security data science for 2017

Data is only as valuable as the decisions it enables

Cloud security. (source: Blue Coat Photos on Flickr).Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode I spoke with Ion Stoica, cofounder and chairman of Databricks. Stoica is also a professor of computer science at UC Berkeley, where he serves as director of the new RISE Lab (the successor to AMPLab). Fresh off the incredible success of AMPLab, RISE seeks to build tools and platforms that enable sophisticated real-time applications on live data, while maintaining strong security. As Stoica points out, users will increasingly expect security guarantees on systems that rely on online machine learning algorithms that make use of personal or proprietary data. As with AMPLab, the goal is to build tools and platforms, while producing high-quality…
Original Post: Data is only as valuable as the decisions it enables

Scaling Scala

Distribution of Scala jobs. (source: DataScience).For more on Scala, check out Dean Wampler’s session, Just enough Scala for Spark, at Strata + Hadoop World San Jose, March 13-16, 2017. Earlier this year, I was building out a data engineering team and had to pick a programming language. Scala seemed like a good choice—we were going to be interacting with Spark quite a bit—but there were a few things that gave me pause. I read through a number of opinions on the subject and came away none the wiser. Everything I read was either obviously biased, five years old, or both. I’m writing this with the hope that it will help anyone in a similar position, clarify and evaluate the value of Scala for data science and engineering teams. In this article, I’ll discuss the state of several major components of…
Original Post: Scaling Scala

The telecommunication industry’s unique position for new revenue opportunities in big data, IoT, and VR

Cellular network. (source: Pixabay).Telcos are facing massive challenges stemming from new customer usage patterns, the rise of over-the-top (OTT) services, and a stagnant subscriber base. In this interview, O’Reilly’s Jon Bruner sat down with Dheeraj Remella, director of solutions architecture at VoltDB, to discuss how telcos must compete in the current industry landscape by using big data to regain value from OTT services and capitalizing on their infrastructure investment with the Internet of Things and augmented and virtual reality. Here are some highlights from their conversation: Using big data to maintain an edge in customer intelligence and discover new revenue streams When you look at the OTT providers [e.g., Skype and YouTube], they know their users in their universe, but all the universes of all the OTT providers are flowing through a cable service provider’s (CSP) network. This means CSPs…
Original Post: The telecommunication industry’s unique position for new revenue opportunities in big data, IoT, and VR