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.
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.
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.
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.
Mar Cabra explains how technology made the Panama Papers investigation possible.
Paul Kent offers an overview of SAS’s participation in open platforms and introduces a new open analytics architecture.
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.
Alistair Croll looks at the sometimes surprising ways that machine learning is insinuating itself into our every day lives.
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.
DJ Patil and Lynn Overmann offer a look at how data science and open data are put to use by the White House.