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.
Roger Magoulas shares insights from O'Reilly's online learning platform that point toward shifts in the systems engineering ecosystem.
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.
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.
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.
Francesc Campoy Flores explores ways machine learning can help developers be more efficient.
Anil Dash asks: How could our processes and tools be designed to undo the biggest bugs and biases of today’s tech?
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.
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.
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.
Amber Case covers methods product designers and managers can use to improve interactions through an understanding of sound design.
Hilary Mason explores the current state of AI and ML and what’s coming next in applied ML.
Chad Jennings explains how Geotab's smart city application helps city planners understand traffic and predict locations of unsafe driving.
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.
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.
Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process
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.
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.
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.
Ted Dunning discusses how new tools can change the way production systems work.
Drew Paroski and Aatif Din share how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
Cassie Kozyrkov explores why businesses fail at machine learning despite its tremendous potential and excitement.
Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more.
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.
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.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning.
Dawn Song explains how AI and deep learning can enable better security and how security can enable better AI.
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel.
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.
Huma Abidi discusses the importance of optimization to deep learning frameworks.
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.
Manish Goyal shows you how to best unlock the value of enterprise AI.
From chaos architecture to event streaming to leading teams, the O'Reilly Software Architecture Conference offers a unique depth and breadth of content.
Tim O'Reilly and Kai-Fu Lee discuss differences in how China and the U.S. approach AI and why AI might give humanity larger purpose.
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days.
Meredith Whittaker says the benefits of AI will only come if we have a clear-eyed perspective on its dark side.
Kai-Fu Lee outlines the factors that enabled China's rapid ascension in AI.
Soups Ranjan describes the machine learning system that Coinbase built to detect potential fraud and fake identities.
Julie Shin Choi reviews real-world customer use cases that take AI from theory to reality.