Project Jupyter co-founder Brian Granger on the JupyterLab project, its potential role in scientific and tech communities, and the expanding role of notebooks.
Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.
Aurélie Pols draws a broad philosophical picture of the data ecosystem and then hones in on the right to data portability.
M. C. Srivas covers Uber's big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.
Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.
Using the music industry as an example, Paul Brook shows how modern information points bring new data that changes the way an organization will make decisions.
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
Darren Strange asks: What part will we each play in what is sure to be one of the most exciting times in computer science?
TensorFlow cookbook materials, source notebooks, Python lectures, and Software Carpentry.
Teresa Tung on building a business case for the Internet of Things.
Bas Geerdink details the technology stack for real-time account forecasting at ING, and outlines how Spark is used for outbound communications.
Access to critical data in real time enables workers to generate insights from large amounts of information.
TSFRESH, 100 days of algorithms, how JupyterHub tamed big science, colorizing photos.
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Executive reading: Why you need to democratize data.
A data-driven study of the complete Internet of Things (IoT) market.
Opinionated Docker stacks, Jupyter Themes, Jupyter in the bank, and Zuckerberg's man in the lab.
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Metadata is central to a modern data architecture.
How to hire the right team and reorganize into a data-driven organization.
A possible solution to the complexities that plague big data projects.
JupyterDay Philly, Harmonics deep dive, Jupyter building blocks, and autoencoded Pokémon.
A look at Apache Kylin’s architecture and features in version 2.0.
Python cheat sheet, open source DL guide, Keen IO, and digital signal processing.
This excerpt from Jake VanderPlas' Python Data Science Handbook
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Three models for how automakers could partner with fleet operating companies to provide autonomous vehicles for on-demand mobility.
Reproducibility, TensorFlow examples, the new NBA, and 30,699 Kobe Bryant shots.
June Andrews talks about simple, cost-effective algorithmic computing at scale.
Kurt Brown discusses services in use, such as Genie, Metacat, Charlotte, and Microbots.
There’s money to be made in exhaust data (not just data exhaust).
Merging the gaps between data science and engineering, and what each side can learn from the other.
Tools, trends, what pays (and what doesn’t) for data professionals in Europe
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Scientific use cases show promise, but challenges remain for complex data analytics.
Andra Keay discusses the five laws of robotics design.
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
Michael Jordan on developing a new platform to support real-time decision-making.
O'Reilly Podcast: Ian Fyfe of Zoomdata on the importance of “speed-of-thought analysis” in modern data environments.
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
The O’Reilly Podcast: Transforming batch storage into streaming data.
Tools from maps to drones respond to crises with increasing speed and accuracy.
The present and future of data integration in the cloud.
Andra Keay outlines principles of good robot design and discusses the implications of implicit bias in our robots.
Desi Matel-Anderson dives into the world of the Field Innovation Team, which uses data to save lives during disasters.
Vijay Narayanan explains how cloud, data, and artificial intelligence are accelerating the genomic revolution.
Rob Craft shares some of the ways machine learning is being used inside of Google.
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.
Dinesh Nirmal discusses how your data can help you build the right cognitive systems to engage with your customers.
Michael I. Jordan explores applications in artificial intelligence.
Ron Bodkin explains how Teradata encourages open source adoption within enterprises.
Transform the way you approach analytics.
Ted Dunning says the internet of things is turning the internet upside down, and the effects are causing all kinds of problems.
Jason Waxman says collaboration between industry, government, and academia is needed to deliver on the promise of AI.
Eric Frenkiel looks at advanced tools and use cases that demonstrate the power of machine learning to bring about positive change.
Mike Olson says without big data and a platform to manage big data, machine learning and artificial intelligence just don’t work.
Watch highlights covering data science, data engineering, data-driven business, and more. From Strata + Hadoop World in San Jose 2017.
Phil Keslin, CTO of Niantic, explains how the engineering team prepared for—and just barely survived—the experience of launching Pokémon GO.
Daphne Koller explains how Coursera is using large-scale data processing and machine learning in online education.
Mix-and-match approaches for visualizing data and interpreting machine learning models and results.