data stuff

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

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

O'Reilly's Jenn Webb chats with Cait O’Riordan, VP of product, music and platforms at Shazam, about the current state of predictive analytics and how Shazam is able to predict the success of a song, and how the Internet of Things affects Shazam’s product life cycles as well as the behaviors of their users. Jenn also chats with Francine Bennett, CEO and cofounder of Mastodon C, about unintentional uses of data for evil and how data scientists can ensure their work stays on the data for good side of that line.

How Shazam Predicts Pop Hits

O'Reilly's Ben Lorica chats with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.

Apache Spark: Powering Applications On-Premise and in the Cloud

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, we’re introduced to random features for large-scale kernel machines, and we take a listener question about the size of computing power in machine learning. 

The Economic Impact of Machine Learning and Using The Kernel Trick To Dig In To Big Data

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

In episode thirteen we talk with Claudia Perlich, Chief Scientist at
Dstillery. We take a look at information leakage in competitions. Plus we take a listener question about trends in data size.

Working With Data and Machine Learning in Advertising

In episode fifteen we talk with Max Welling, of the University of Amsterdam and University of California Irvine. We talk with him about his work with extremely large data and big business and machine learning. We take a listener question about the relationship between machine learning and artificial intelligence. Plus, we get an introduction to change point detection. 

Really Really Big Data and Machine Learning in Business

O'Reilly's Ben Lorica chats with VoltDB co-founder Scott Jarr about how VoltDB’s hybrid transaction, analytic system allows for real-time analytics and personalization of data across various industries.

How Real-Time Analytics Integrates With Our Connected World

Paco Nathan and Jesse Anderson on the evolution of the data training landscape.

O’Reilly’s Ben Lorica chats with Paco Nathan, director of O’Reilly Learning, and Jesse Anderson, technical trainer and creative engineer at Confluent, about the training landscape in the big data ecosystem, their teaching techniques and particular content they choose, and a look at some expected future trends.

Training in the Big Data Ecosystem

Host and O'Reilly chief data scientist Ben Lorica interviews Nidhi Aggarwal, whose background is in hardware and software, and whose experience ranges from work in engineering, consulting, research, and entrepreneurship.

Ask Your Data New Questions

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

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

Emil Eifrem, CEO and co-founder of Neo Technology, on the early days of NoSQL, applications of graph databases, cloud computing, and company culture in the U.S. and Sweden.

Graph Databases are Powering Mission-Critical Applications

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.

data stuff

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