We need to build organizations that are self-critical and avoid corporate self-deception.
David Hayes explains why adding a manageable dose of actionable intelligence to your operations management workflow can save you time and aggravation.
Open Source, Milspec Origami, Machine Says No, and Golang
Oracle's Kyle York and Netra's Richard Lee discuss Netra’s high-performance computing environment.
Tracy Lee helps you think differently about how to increase diversity in technology with open source.
Julia Grace shares how she learned to rapidly scale herself and her leadership team during a period of hypergrowth at Slack.
Focusing on a mix of artificial, scientific, and environmental sensing data, Aurelia Moser explores fantasy and farcical mapping.
Kyle Kingsbury explores anomalies in three distributed systems and shares strategies for correctness testing using Jepsen.
Bryan Liles explains how to evaluate and integrate new declarative application management practices into continuous integration pipelines.
Dave Andrews explains how to wield the power of a global 50 Tbps application delivery network to ensure maximum availability during and after a change.
Nicole Forsgren shares results and stories behind high-performing technology-driven teams and organizations.
Brendan Eich asks what it would mean to the web if we start building products, apps, and systems that are private by default.
Tamar Bercovici details how the team at Box has constructed its database stack to handle an ever-growing query load and data set.
Lin Clark explains what browser vendors need to do over the next few years to ensure their browsers, and the web itself, meet upcoming demands.
Scott Davis explains why accessibility should be just as important to you as a mobile design strategy was 10 years ago.
Martin Woodward shares key data points from Microsoft's journey to DevOps.
Cory Doctorow fields questions on the future of the web, privacy, and net neutrality.
Cherie Wong shares common developer pain points and recipes to solve them using AWS.
Watch highlights covering infrastructure, DevOps, security, and more. From the O'Reilly Velocity Conference in San Jose 2018.
Javier Garza details the ingredients you need to build and deliver an app your users will love.
Natalie Silvanovich discusses the link between feature complexity, developer error, and security vulnerabilities.
Kris Nova looks at the four metrics that help you decide if running stateful applications in Kubernetes is worth the risk.
Cory Doctorow says the right to configure technology is the signature right of the 21st century.
Kyle York explores the scale, complexity, and volatility of the internet and the risk it poses to your applications and infrastructure.
Renee Orser explains how to monitor the human networks within your engineering teams using models similar to your distributed technology systems.
A look at a few ways to evaluate whether or not a design achieves what it set out to do.
Practical advice for software engineers and security consultants.
The benefits of modeling data as events as a mechanism to evolve our software systems.
How to identify when a fit has been achieved, and how to exit the explore stage and start exploiting a product with its identified market.
Taking blockchain technology private for the enterprise.
An overview of common design patterns for navigation that will ensure users can find and use features in an application.
An overview and framework, including tools that can be used to enable automation.
The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.
Find new ways to gain insight into how your users interact.
Cast your vote for the top open source projects and communities through June 29.
Use cases of AI and ML to help businesses build better defenses today and in the near future.
This collection of AI resources will get you up to speed on the basics, best practices, and latest techniques.
Get hands-on training in machine learning, Python, Java, Kubernetes, product management, and many other topics.
The O’Reilly Podcast: Tammy Butow and Annie Lau on the importance of creating a culture of learning.
Considerations based on experience with Fortune 500 clients.
Why model development does not equal software development.
When we finally find the best use cases for blockchains, they may look like nothing we would have expected.
Learn design best practices and where conversational AIs are headed in the future.
A commitment to multi-modal learning is better than grasping for single-modality solutions that don’t deliver.
Recipes that deal with various aspects of troubleshooting, from debugging pods and containers, to testing service connectivity, interpreting a resource’s status, and node maintenance.
Ben Brown on why messaging design will become as important as responsive design.
Louise Beaumont explores the five characteristics of companies that choose to succeed.
Christine Foster discusses how today’s academic papers turn into tomorrow’s data science.
Martha Lane Fox considers the unintended consequences of technology.
Having worked in both research and industry, Mikio Braun shares insights into what's the same, what's different, and how deep learning might change the game.
Zubin Siganporia explains how the KISS principle (“Keep It Simple, Stupid”) applies to solving problems and convincing end-users to adopt data-driven solutions to their challenges.
One of our goals is to bring Jupyter’s enterprise use cases and practices into one place.
The O’Reilly Data Show Podcast: A special episode to mark the 100th episode.
Successful projects will think seriously about what blockchains mean, and how to use them effectively.
The personal robot temi refactors robotic human behaviors we encounter in the “iPhone Slump,” and moves those back to actual robots.
Jean-François Puget explains why human context should be embraced as a guide to building better and smarter systems.
Pierre Romera explores the challenges in making 1.4 TB of data securely available to journalists all over the world.
Ben Lorica looks at the problems we’re facing as we collect and store data, particularly when our machine learning models require huge amounts of labeled data.
May 25 is an important day for data protection in the EU and elsewhere. Alison Howard explains how Microsoft has prepared for May 25 and beyond.