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.
The O’Reilly Data Show Podcast: Vikash Mansinghka on recent developments in probabilistic programming.
Rather than hiring data scientists from outside, consider training your proto data scientists.
It's important in this age of big data to return the original meaning of serendipity and talk about it as a skill.
The O’Reilly Data Show Podcast: Michael Franklin on the lasting legacy of AMPLab.
Deeper neural nets often yield harder optimization problems.
O'Reilly Podcast: Working with databases that go beyond traditional models.
Performing business analytics on the data lake using next-gen open source tools.
The O’Reilly Data Show Podcast: Dafna Shahaf on information cartography and AI, and Sam Wang on probabilistic methods for forecasting political elections.
A look at the data pipeline architecture for five key NERSC projects.
Close the time gap between analysis and action to bring about the next wave of improvements in efficiency and reliability—and magic.
Python and R are widely accepted as logical languages for data science—but what about Go?
O'Reilly Podcast: Qubole founder Ashish Thusoo on the importance of self-service data.
The O’Reilly Data Show Podcast: Christopher Nguyen on the early days of Apache Spark, deep learning for time-series and transactional data, innovation in China, and AI.
The O’Reilly Data Show Podcast: Natalino Busa on developments in feature engineering and predictive techniques across industries.
This report explores how political data science helps to drive everything from overall strategy and messaging to individual voter contacts and advertising.
The O’Reilly Data Show Podcast: Shaoshan Liu on perception, knowledge, reasoning, and planning for autonomous cars.
Apache Arrow makes it possible to use multiple languages and heterogeneous data infrastructure.
Why cross-channel analytics are crucial to empowering business teams with a behavioral view of your customer.
Start planning now to reap the many benefits of connected manufacturing.
Watch highlights covering data science, big data, data in the enterprise, and more. From Strata + Hadoop World in New York 2016.
Will machine learning give us better eyesight? Joseph Sirosh offers a surprising story about how machine learning, population data, and the cloud are coming together to reimagine eye care in India.
We live in a 3D world and we need to enable data interaction from all perspectives. Immersive visualization does just that.
Cloudera CEO Tom Reilly and Nielsen Global CTO James Powell discuss the dynamics of Hadoop in the cloud, what to consider at the start of the journey, and how to implement a solution that delivers flexibility and meets enterprise requirements.
Chad Jennings demonstrates BigQuery's capabilities and announces several new features.
What explains the gap between what machines do well and what people do well? And what needs to happen before machines can match the flexibility and power of human cognition?
Todd Brannon says the need for performance crosses the big data ecosystem—from the edge, to the server, to the analytics software.
Paul Kent offers an overview of SAS’s participation in open platforms and introduces a new open analytics architecture.
DJ Patil and Lynn Overmann offer a look at how data science and open data are put to use by the White House.
Alistair Croll looks at the sometimes surprising ways that machine learning is insinuating itself into our every day lives.
Mar Cabra explains how technology made the Panama Papers investigation possible.
An analytics database can offer performance and scalability advantages.
Ron Bodkin explains how Teradata spurs open source adoption inside enterprises through a range of initiatives.
What’s good for the country is good for your data. Consider what the next four years will look like for your organization.
Sriram Vishwanath outlines areas where data science can have a significant impact on health care, and dispels myths where data science use contradicts realities within the health care ecosystem.
Susan Woodward discusses venture outcomes—what fraction make lots of money, which just barely return capital, and which fraction fail completely
Mike Olson discusses the new dynamics of big data and how a renewed approach focused on where, who, and why can lead to cutting edge solutions.
The power of AI and advanced analytics is realized from the ability to analyze and compute large data sets from varied devices and locations. Learn how collaboration and openness are key elements driving this innovation.
With your most precious commodity, data, increasing at an alarming rate, it is essential that it be a component to your deepest insights.
Watch keynotes from Strata + Hadoop World in New York City.
Learn the challenges oil and gas companies face when collecting data and how they mitigate short-term operational risk and optimize long-term reservoir management.
The anatomy of an architecture to bring data science into production.
The O’Reilly Data Show Podcast: Dean Wampler on streaming data applications, Scala and Spark, and cloud computing.
The destination and rules of the road are clear; the route you choose to get there makes a huge difference.
Leading data-driven organizations point out five common pitfalls.
The O’Reilly Data Show Podcast: Michael Li on the state of data engineering and data science training programs.
Results and analysis from O'Reilly's fourth annual survey of data science professionals.
The O’Reilly Data Show Podcast: Rana el Kaliouby on deep learning, emotion detection, and user engagement in an attention economy.