Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.
Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.
Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.
Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.
Ruchir Puri explains why trust and transparency are essential to AI adoption.
Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.
Our bad AI could be the best tool we have for understanding how to be better people.
A new report examines the state of infrastructure and anticipated near-term developments through the eyes of infrastructure experts.
Jaana Dogan explains why Google teaches its tracing tools to new employees and how it helps them learn about Google-scale systems end to end.
Michael Bernstein offers an unflinching look at some of the fallacies that developers believe about marketing.
Laura Thomson shares Mozilla’s approach to data ethics, review, and stewardship.
Roger Magoulas shares insights from O'Reilly's online learning platform that point toward shifts in the systems engineering ecosystem.
Tammy Butow explains how companies can use Chaos Days to focus on controlled chaos engineering.
Practical techniques to ensure developers can actually do the things you want them to do using your API.
Watch highlights from expert talks covering DevOps, SRE, security, machine learning, and more.
Kris Beevers examines the trade-offs between risk and velocity faced by any high-growth, critical path technology business.
Anil Dash asks: How could our processes and tools be designed to undo the biggest bugs and biases of today’s tech?
Jessica McKellar draws parallels between the free and open source software movement and the work to end mass incarceration.
Dave Rensin explains why DevOps and SRE make each other better.
Kavya Joshi says performance theory offers a rigorous and practical approach to performance tuning and capacity planning.
Laurent Gil shares the latest cybersecurity research findings based on real-world security operations.
Francesc Campoy Flores explores ways machine learning can help developers be more efficient.
How risk reduction makes sure bad things happen as rarely as possible.
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
The World Economic Forum’s 2018 jobs report limits research to a narrow range of the workforce.
Getting DataOps right is crucial to your late-stage big data projects.
How we can put privacy at the heart of our design processes.
Using advanced Docker Compose features to solve problems in larger projects and teams.
The economy we want to build must recognize increasing the value to and for humans as the goal.
Exploring use cases for the two tools.
Asking good design questions will elucidate problems and opportunities.
Dinesh Nirmal explains how AI is helping supply school lunch and keep ahead of regulations.
Brain-based human-machine interfaces: New developments, legal and ethical issues, and potential uses
Amanda Pustilnik highlights potential applications of data from new technologies that capture brain-based processes.
Amber Case covers methods product designers and managers can use to improve interactions through an understanding of sound design.
Ziya Ma discusses how recent innovations from Intel in high-capacity persistent memory and open source software are accelerating production-scale deployments.
Julia Angwin discusses what she's learned about forgiveness from her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.
Hilary Mason explores the current state of AI and ML and what’s coming next in applied ML.
Jacob Ward reveals the relationship between the unconscious habits of our minds and the way that AI is poised to amplify them, alter them, maybe even reprogram them.
Chad Jennings explains how Geotab's smart city application helps city planners understand traffic and predict locations of unsafe driving.
Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process
We should invest at least as much time in understanding our customers as we do in optimizing our product development process.
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
DD Dasgupta explores the edge-cloud continuum, explaining how the roles of data centers and cloud infrastructure are redefined through the mainstream adoption of AI, ML, and IoT technologies.
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
Ted Dunning discusses how new tools can change the way production systems work.
Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more.
Cassie Kozyrkov explores why businesses fail at machine learning despite its tremendous potential and excitement.
Executives from Cloudera and PNC Bank look at the challenges posed by data-hungry organizations.
Joseph Lubin explains how Ethereum can help with new innovations like cryptocurrencies, automated and self-executing legal agreements, and self-sovereign identity.
Drew Paroski and Aatif Din share how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.
Poll results reveal where and why organizations choose to use containers, cloud platforms, and data pipelines.
It has become much more feasible to run high-performance data platforms directly inside Kubernetes.
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows.
Hagay Lupesko explores key trends in machine learning, the importance of designing models for scale, and the impact that machine learning innovation has had on startups and enterprises alike.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Peter Norvig says one of the most exciting aspects of AI is the diversity of applications in fields far astray from the original breakthrough areas.