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.
Dinesh Nirmal discusses how your data can help you build the right cognitive systems to engage with your customers.
Desi Matel-Anderson dives into the world of the Field Innovation Team, which uses data to save lives during disasters.
Rob Craft shares some of the ways machine learning is being used inside of Google.
Andra Keay outlines principles of good robot design and discusses the implications of implicit bias in our robots.
Vijay Narayanan explains how cloud, data, and artificial intelligence are accelerating the genomic revolution.
Ron Bodkin explains how Teradata encourages open source adoption within enterprises.
Michael I. Jordan explores applications in artificial intelligence.
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.
Transform the way you approach analytics.
Mike Olson says without big data and a platform to manage big data, machine learning and artificial intelligence just don’t work.
Jason Waxman says collaboration between industry, government, and academia is needed to deliver on the promise of AI.
Watch highlights covering data science, data engineering, data-driven business, and more. From Strata + Hadoop World in San Jose 2017.
Ted Dunning says the internet of things is turning the internet upside down, and the effects are causing all kinds of problems.
Eric Frenkiel looks at advanced tools and use cases that demonstrate the power of machine learning to bring about positive change.
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.
How we created an illustrated guide to help you find your way through the data landscape.
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.
Transform your basemaps using CARTO and PostGIS.
Four leading figures in transportation and logistics offer a glimpse into what they see coming around the corner.
An ongoing data governance program provides intellectual and institutional grounding to adhere to a company's strategic plan.
4 questions to spark ethical decisions about data.
Flash flood prediction using machine learning has proven capable in the U.S. and Europe; we're now bringing it to East Africa.
An interview with Greg Meddles, technical lead for healthcare.gov.
The O’Reilly Data Show Podcast: Parvez Ahammad on minimal supervision, and the importance of explainability, interpretability, and security.
Using a data-driven analysis to understand IoT technology adoption.
The better prepared you are to utilize all the data in your data lake, the more likely you are to be successful.
Your company is probably already doing AI and machine learning, but it needs a road map.
How to map out a plan for finding value in data.
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks.
Understanding the FTC’s role in policing analytics.
Sara M. Watson from Digital Asia Hub discusses the state of personalization and how it can become more useful for consumers.
Data governance is straightforward; data strategy is not.
How Project Jupyter got here and where we are headed.
The O’Reilly Data Show Podcast: Adam Gibson on the importance of ROI, integration, and the JVM.
Validating your data requires asking the right questions and using the right data.
A peek into the clickstream analysis and production pipeline for processing tens of millions of daily clicks, for thousands of articles.
Careful choices in data collection and architecture can reduce burdens.
What data scientists need to know about production—and what production should expect from their data scientists.
Best practices and scalable workflows for reproducible data science.
Putting deep learning into practice with new tools, frameworks, and future developments.
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
Drew Paroski and Gary Orenstein on the rapid spread of machine learning and predictive analytics
From AI to uncertain political outlooks: What's on our radar.
From deep learning to decoupling, here are the data trends to watch in the year ahead.
The O’Reilly Data Show Podcast: A look at some trends we’re watching in 2017.
How bots, threat intelligence, adversarial machine learning, and deep learning are impacting the security landscape.
The O’Reilly Data Show Podcast: Ion Stoica on building intelligent and secure applications on live data.
Evaluating the state and development of Scala from a data engineering perspective.
The telecommunication industry’s unique position for new revenue opportunities in big data, IoT, and VR
Telcos must regain value from over-the-top services and develop new sources of revenue by leveraging their data and infrastructure.
The O’Reilly Podcast: John Thuma on how businesses can get more than “what happened” from their data.
The O’Reilly Podcast: Bob Montemurro on planning data systems to match needs.
Technical and policy considerations in combatting algorithmic bias.
Learning to act based on long-term payoffs.