What Kaggle has learned from almost a million data scientists
Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.
Data
Our take on the ideas, information, and tools that make data work.
Highlights from Strata Data Conference in London 2017
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
The data subject first?
Aurélie Pols draws a broad philosophical picture of the data ecosystem and then hones in on the right to data portability.
Real-time intelligence gives Uber the edge
M. C. Srivas covers Uber's big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.
Language understanding remains one of AI’s grand challenges
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Data preparation in the age of deep learning
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Bringing interactive BI to big data
A look at Apache Kylin’s architecture and features in version 2.0.
Scaling machine learning
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
2017 European Data Science Salary Survey
Tools, trends, what pays (and what doesn’t) for data professionals in Europe
Architecting and building end-to-end streaming applications
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Becoming a machine learning engineer
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
Natural language analysis using Hierarchical Temporal Memory
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
Becoming smarter about credible news
Cloudera CEO Tom Riley and Thomson Reuters VP of R&D Khalid Al-Kofahi discuss big data's role in chasing down leads, verifying sources, and determining what's newsworthy.
Making good robots
Andra Keay outlines principles of good robot design and discusses the implications of implicit bias in our robots.
Ray: A distributed execution framework for emerging AI applications
Michael I. Jordan explores applications in artificial intelligence.
Data in disasters: Saving lives and innovating in real time
Desi Matel-Anderson dives into the world of the Field Innovation Team, which uses data to save lives during disasters.
Machine learning at Google
Rob Craft shares some of the ways machine learning is being used inside of Google.
Applying data and machine learning to scale education
Daphne Koller explains how Coursera is using large-scale data processing and machine learning in online education.
Launching Pokémon GO
Phil Keslin, CTO of Niantic, explains how the engineering team prepared for—and just barely survived—the experience of launching Pokémon GO.
Highlights from Strata + Hadoop World in San Jose 2017
Watch highlights covering data science, data engineering, data-driven business, and more. From Strata + Hadoop World in San Jose 2017.
The machine learning renaissance
Mike Olson says without big data and a platform to manage big data, machine learning and artificial intelligence just don’t work.
Ideas on interpreting machine learning
Mix-and-match approaches for visualizing data and interpreting machine learning models and results.
Saving the world—or at least the world’s scientific and government data
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
Deep learning that’s easy to implement and easy to scale
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.